Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

Abstract

One of the most important concerns about recommender systems is the filter bubble phenomenon. While recommender systems try to personalize information, they tighten the filter bubble around the users and deprive them of a wide range of content. To overcome this problem, one can diversify the personalized recommendation list. A diversified list usually presents a broader content to the user. Session-based recommender systems are types of recommenders in which only the current session of the user is available, and therefore, they should recommend the next item given the items in the current session. While diversifying conventional recommender systems has been well assessed in the literature, it has gained less attention in session-based recommenders. Diversity and accuracy usually have a negative correlation, i.e., by improving one the other one will be declined. In this study, we propose diversity and accuracy enhancing approaches based on sequential rule mining and session-based k-nearest neighbor methods. Finally, we propose a performance balancing approach that improves both the diversity and accuracy of these session-based recommender systems. We demonstrate the performance of the proposed methods on four music recommender datasets.

Introduction

Electronic services are growing rapidly and people tend more and more to shop, read, listen, and communicate online. These activities generate interactions between users and items in different contexts which can be used to personalize user experience in online environments. Online service providers use recommender systems (RSs) to model user preferences based on previous user interactions and to recommend services to fulfill user needs.

RSs are classically categorized in two broad categories: content-based filtering (CB) and collaborative filtering (CF). While CB only uses a user profile and content metadata to recommend items, CF considers interactions of other users in its predictions. In some domains, users do not have a profile in the system, and therefore, the only relevant information is the current session. A session starts when a user enters a website, continues when she navigates through it, and finishes when she leaves it. Therefore, personalization can only depend on the events of the current session. In this case, Session-Based Recommender Systems (SBRSs) are used to predict the next item given the previous items in the current session. SBRSs are applied in many domains such as music and video streaming websites, news aggregators, and web-shops. For instance, in a music streaming website, an SBRS can be used to recommend the next song given the songs that an anonymous user has already listened to.

In many applications, there is side information associated with the recommendable items. This information can be used to boost accuracy in RSs. For instance, a CF recommender system can be hybridized with a CB method to boost accuracy. Although this helps RSs to recommend more relevant items, it escalates the filter bubble phenomenon. This phenomenon occurs when RSs try to filter relevant information and recommend it to the users, but, while doing so, deprive them of diverse set of content. In this situation, the user may receive several recommended items with relatively similar content. For instance in a news aggregator website, an RS may narrow the range of recommended news topics by only focusing on news that is relevant according to the user histories [1]. This may help accuracy, but it tightens the filter bubbles around users. Overcoming the filter bubble phenomenon is an important challenge in RSs [2]. Instead of considering only the relevance, one should consider also the diversity in the recommendation list. A diversified recommendation list is less probable to contain very similar and sometimes redundant content. For example, in a music recommendation, task diversification provides songs in different genres and from several artists to widen the content bubbles around users. Since the only useful information in SBRS is the sequence of events in the current session, the filter bubble problem is even more concerned as RSs should retrieve relevant items based on a limited number of interactions. Diversity should be considered in the recommendation list to avoid recommending redundant content to the user; however, to the best of our knowledge, current SBRS algorithms only focus on providing relevant recommendations, ignoring diversity. Empirical studies investigating the best strategy to include diversity into SBRSs are mostly lacking in the literature.

Although diversification mitigates the filter bubble phenomenon, it usually degrades accuracy. Therefore, the challenging task is to find a balance for these two performance dimensions. An important property of the sessions (users) in the platform is that they are different in terms of profile diversity: some users are focused users, who are interested in specific content, and some users prefer to interact with a broader range of content. Therefore, to balance the two performance measures and to increase the overall performance, we propose to personalize the diversity versus accuracy trade-off of recommendation lists. For instance, in a music streaming platform, the recommender can adapt the diversity and accuracy of its recommendations based on the genre diversity of previous songs in the active session. If the anonymous user is interested in only one genre (focused user), the recommender should provide more accurate recommendations from the same genre. On the other hand, if the user has interacted with a wider range of genres in the session, the recommender should provide more diverse recommendations to cover the broad preferences of the user.

The contribution of this paper is threefold: first, we introduce diversity in SBRSs to burst the filter bubbles around anonymous users. The diversity-aware SBRSs provide a wider range of content in their recommendations. Second, we hybridize SBRSs with items metadata to boost their accuracy. Finally, we demonstrate the possibility of enhancing the overall performance of SBRSs in both accuracy and diversity by personalizing the diversity vs accuracy balance of recommendations. Two state-of-art methods in SBRS are extended with diversity and accuracy enhancing strategies. These strategies are compared w.r.t. diversity, accuracy, and computational time, using four real-life datasets from the music recommendation domain.

In the following, related studies about diversity definition and measurement, SBRSs, diversity enhancing approaches, and diversity-aware SBRSs are reviewed (“Related Work”). Then, in “Enhancing Diversity and Accuracy in Session-based Recommender Systems”, the proposed strategies to enhance diversity and accuracy of two SBRSs are explained. In “Datasets and Experiment Set-Up”, four music datasets are described, and the experimental settings in designing and testing the proposed models are discussed. Next, the performance of several SBRS baselines is compared, and the results of applying different diversity and accuracy enhancing strategies based on several performance measures are assessed in “Results” and discussed in “Discussion”, before concluding in “Conclusion”.

Related Work

Diversity Definition and Measurement

Diversification was first concerned in the information retrieval (IR) community to disambiguate the user’s query [3]. A classic example is the search term jaguar which may refer to the animal, the car, or the guitar. If the retrieved document list is more diverse, the chance of covering user’s real intent in the retrieved list would be higher. Therefore, considering and optimizing diversity in a ranked list of retrieved documents using document features have attracted a great deal of attention in the IR community [4].

In recommendation systems, diversity is a way to address the filter bubble phenomenon. For instance, in a movie streaming website, the diversity of a recommended list that contains action, drama, and comedy movies has higher diversity than a recommended list containing only action movies. The diversity of a recommendation list is commonly defined as the average pairwise distance between items in the list [5]. This diversity measure, which is also called intra-list diversity (ILD), shows to what extent the user will receive a diverse range of items in the recommended list. Calculating pairwise distance between items depends on feature representations of items and the distance measure. For instance, features can be text-based features [6, 7], genres [8, 9], taxonomy [10, 11], rating vector [12,13,14], neighborhood of items in item-based CF or neighborhood of users in user-based CF [15], and latent features in model-based CF [16,17,18,19]. Distance measures can be taxonomy-based [10], cosine, Jaccard, Hamming distance, and Pearson correlation [3].

Session-Based Recommender Systems

SBRSs are common in domains in which a user’s profile is missing and the only available information is the current session. The main task in this setting is predicting the next item given the sequence of events in the current session. SBRSs can be categorized to pattern/rule-based, sequential pattern-based, nearest neighbors-based, Markov chain-based, factorization-based, and neural network-based methods [20, 21]. Based on a comprehensive evaluation on state-of-art SBRSs using several music, e-commerce, and news datasets, Ludewig and Jannach [20] concluded that simple methods such as sequential rule mining and session-based neighborhood methods surprisingly have better performance w.r.t. accuracy measures compared to more complex methods. These results are also supported by other studies  [22,23,24,25,26,27,28].

Although neighborhood methods are relatively simple, they perform surprisingly well. The simplest version of neighborhood methods, i.e, item-based KNN [29] (IKNN), has been frequently used as a baseline in the SBRS literature. In this method, only the last item of the current session is used to form a neighborhood. Session-based KNN (SKNN) [23] methods are a generalization of IKNN that use the whole sequence of events in the current session to form the neighborhood. There are some variants of SKNN that put more emphasis on recency of events in the session (vector multiplication SKNN), position of events in the session (sequential SKNN), and also a more restrictive position aware version (sequential filter SKNN) [20].

There are some SBRSs that use artificial neural networks to model sequences in sessions. Hidasi et al. [30] proposed a Recurrent Neural Network (RNN) model using Gated Recurrent Units (GRU) to recommend the next item in the session. This model was extended by changing the loss function and sampling approach that shows better performance compared to the previous model [31]. Li et al. [32] introduced NARM method that uses an RNN-based network with attention mechanism to generate recommendations. This network models the sequential information and the main purpose of the session to form recommendations. Wang et al. [33] proposed an attention-based transaction embedding model (ATEM) for next item recommendation task that learns an attentive context embedding to emphasize more on relevant items in the user session. Sheu and Li [34] used a context-aware graph embedding framework for a news SBRS. This framework constructs a knowledge graph based on the news entities to improve article embeddings generated by graph neural networks. Liu and Zheng [35] proposed TailNet model which is an SBRS that gives more attention to the long-tail items to make the recommendations more serendipitous. Wu et al. [36] introduced an SBRS with graph neural network (SR-GNN). They modeled the session sequences as graph structured data. Using this structure, the model can represent the current interest and the global preference of the active session.

Enhancing Diversity in Recommendations

Generally speaking, there are two approaches to enhance diversity of RSs: Re-ranking approach and diversity modeling.

Re-ranking Approach

Re-ranking is a post-filtering approach which re-ranks an initial list generated by a baseline method to achieve better performance based on some additional objectives. A greedy approach using Maximal Marginal Relevance [37] is the most common form of re-ranking in the literature. Different objectives and distance functions are used to greedily re-rank the initial list [5, 10, 12, 17, 38, 39]. There are some other studies that use constrained optimization [40, 41] and multi-objective optimization [14] instead of a greedy approach to re-rank the initial list.

Diversity Modeling

The other way of enhancing diversity of a recommendation list is to consider a diversity component in the main algorithm. Said et al. [42] proposed the k-furthest neighbor method to generate a more diverse recommendation list compared to the k-nearest neighbor method. Shi et al. [19] introduced Latent Factor Portfolio model in which portfolio theory [43] and matrix factorization were used to diversify the result. Some other studies manipulated the original loss functions of algorithms to enhance diversity. Su et al. [16] proposed the set-based Bayesian Personalized Ranking (BPR) as an extension of BPR [44] in which, instead of using pairs of positive and negative items, they used pairs of positive and negative item sets in the BPR loss function. They considered the diversity of these item sets in their predicted ratings. Hurley [45] adds the distance of two sampled items in the loss functions of the RankALS [46] and RankSGD [47] optimization approaches to enhance diversity. In another study [48], some regularization terms are proposed to be added to the RankALS and RankSGD approaches to generate a more diverse recommendation list. Wang et al. [49] proposed the mixture-channel purpose routing networks (MCPRNs) that can cover multiple purposes of the user in the session by recommending a more diverse recommendation list. They showed that their model has better accuracy and diversity compared to the baselines.

Boosting Accuracy and Diversity in Session-Based Recommender Systems

There are some studies that hybridized SBRSs with item features to boost their accuracy. Tan et al. [50] improved the performance of Recurrent Neural Network (RNN)-based recommenders by using item features. Moreira et al. [51] studied the effect of content on a neural SBRS and they showed that hybridizing an SBRS with item features boosts the accuracy measures. Wang et al. [52] proposed the neural network-based comprehensive transaction embedding model (NTEM) that uses a shallow and wide network. This network uses the explicit relevance between items from the session data and the implicit relevance from features of items to recommend the most relevant next items and consequently to boost accuracy.

To the best of our knowledge, diversity enhancing approaches in SBRS are very limited in the literature. Just like the concept of diversity that was originated from the IR community, xQuAD was introduced in the IR community [53] and later applied in recommendation systems [54]. xQuAD is a greedy re-ranking approach that uses an intent-oriented diversity component in the re-ranking objective function. Anelli et al. [55] introduced a variant of xQuAD in which they changed the diversity component in the greedy optimization approach of xQuAD to make it time and session aware. Esmeli et al. [56] introduced a variant of IKNN using not only the last item but also the session context in score calculations. They also used the dissimilarity of recommendable items based on the whole session items in the final score function. At the end, instead of evaluating the proposed method in terms of diversity, they showed that, using this approach, they can improve the precision and recall in an e-commerce dataset.

Enhancing Diversity and Accuracy in Session-Based Recommender Systems

In this section, we propose diversity and accuracy enhancing strategies for two SBRSs. We used the result of the comprehensive evaluation of [20] (which is confirmed in our comparison of baselines in “Results“) to select SBRS methods, namely sequential rule mining (SR) and SKNN. These methods are selected as they have competitive performances with low computational costs. We propose three diversity or accuracy boosting strategies: weight of candidate (“Enhancing Diversity and Accuracy in Sequential Rule mining” and “Enhancing Diversity and Accuracy in Session-Based k-Nearest Neighbors”), neighbor selection (“Enhancing Diversity and Accuracy in Session-Based k-Nearest Neighbors”) and weight of neighbors (“Enhancing Diversity and Accuracy in Session-Based k-Nearest Neighbors”). In addition, we also propose a balancing approach that improves both the diversity and accuracy (“Balancing Diversity and Accuracy”). In “Enhancing Diversity and Accuracy in Sequential Rule mining”, only weight of candidate strategy used to enhance the diversity or accuracy of SR, while in “Enhancing Diversity and Accuracy in Session-Based k-Nearest Neighbors”, all the three proposed strategies are applied to boost the diversity or accuracy of SKNN. Finally, in “Balancing Diversity and Accuracy”, we explain how one can enhance the overall diversity and accuracy at the same time in these two SBRSs, by adapting the strategy based on the diversity of individual sessions. As the greedy re-ranking approach is commonly used in literature to enhance diversity, we consider it in our experiments as a baseline.

Enhancing Diversity and Accuracy in Sequential Rule Mining

Sequential rule mining (SR) identifies events that frequently co-occurred in the past to recommend the next item considering the previous items visited by the user. This method is a variant of association rule mining (AR) in which the order of frequent items in the rules is important and the further apart two frequent items are the less weight is assigned to these frequent items. Using this approach and the notation used by [22], the predicted score for a candidate item and the current session is:

$$ \hat{r}_{{{\text{SR}}}} (i,s) = 1_{R} (r_{{j,i}} ) \times w_{{j,i}} . $$
(1)

In Eq. (1), \(\hat{r}_{_{SR}}(i,s)\) is the predicted score for candidate item i and session s, R is the set of rules of item co-occurrences in previous sessions, j is the last item of s, \(r_{j,i}\) is the rule for item j, and i, \(1_{R}(r_{j,i})\) is an indicator function that verifies whether there is a rule for item j and i in R and \(w_{j,i}\) is the weight of the rule. To calculate \(w_{j,i}\), instead of only counting the number of co-occurrences of items in previous sessions as the weight, SR penalizes the weight based on the distance of these items.

We propose the weight of candidate strategy to enhance the accuracy or diversity of SR. To enhance diversity in SR, one can assign more weights to candidate items that add more diversity to the current session. This weight (\(\delta _{i,s}\)) can be computed using Eq. (2). An item that has more dissimilarity with the current session is more likely to make the final recommendation list more diverse. Using this weight, diversity-aware SR can be formulated in Eq. (3):

$$\delta _{{i,s}} = \frac{1}{{|s|}}\sum\limits_{{j \in s}} {\text{d}} {\text{ist}}_{c} (i,j)$$
(2)
$$\hat{r}_{{{\text{SR}}}} (i,s) = 1_{R} (r_{{j,i}} ) \times w_{{j,i}} \times \delta _{{i,s}} .$$
(3)

Using a fairly similar approach, one can increase accuracy of SR by replacing \({\text{dist}}_{c} (i,j)\) with \({\text{sim}}_{c} (i,j)\) in Eq. (2). In this setting, SR assigns more weight to an item that is more similar (content-wise) to the current session. \({\text{sim}}_{c} (i,j)\) is the content similarity and \({\text{dist}}_{c} (i,j)\) is the content dissimilarity of item i and j. \({\text{sim}}_{c} (i,j)\) and \({\text{dist}}_{c} (i,j)\) can be calculated using a similarity measure such as the cosine similarity. Obviously, only one of these approaches (enhancing diversity or accuracy) is possible at the same time.

Enhancing Diversity and Accuracy in Session-Based k-Nearest Neighbors

SKNN is a memory-based method that uses all of the items of the current session to find the k-nearest neighbor sessions. To predict the score of a candidate item, SKNN uses the session similarity of neighbor sessions with the current session. Here, the session similarity (\({\text{sim}}_{s} (s,n)\)) is the similarity between binary vectors of sessions over the item space. This predicted score can be calculated using Eq. (4):

$$ \hat{r}_{{{\text{SKNN}}}} (i,s) = \sum\limits_{{n \in N_{s} }} {\text{s}} {\text{im}}_{s} (s,n) \cdot 1_{n} (i). $$
(4)

In which i is the candidate item, s is the current session for which we want to predict the next item, \(N_{s}\) is the set of current session neighbors, \({\text{sim}}_{s} (s,n)\) is the similarity of current session s and one of its neighbors n, and \(1_{n}(i)\) is an indicator function to check whether item i exists in session n. \({\text{sim}}_{s} (s,n)\) can be calculated using a similarity measure such as cosine, Jaccard, or Pearson correlation.

We propose three strategies that can be added to SKNN methods to enhance either diversity or accuracy: neighbor selection, weight of neighbors, and weight of candidate item. The predicted score for a diversity or accuracy boosting SKNN can be calculated using Eq. (5):

$$ \hat{r}_{{{\text{SKNN}}}} (i,s) = \sum\limits_{{n \in N_{s}^{\prime } }} {w_{n} } (s,n) \cdot \delta (i,s) \cdot 1_{n} (i). $$
(5)

Neighbor Selection

One way to recommend a more diverse recommendation list is to use neighbors that are more diverse. Therefore, in Eq. (5), instead of using similarity as the only criterion to form neighborhood of s, neighbors (\(N^{\prime }_{s}\)) will be selected based on their similarity to the current session and their diversity, i.e., instead of \({\text{sim}}_{s} (s,n)\), \({\text{sim}}_{s} (s,n) \times {\text{diversity}}(n)\) is used. diversity(n) can be computed using the average intra-list distance (ILD) measure (Eq. 6).

$$ {\text{diversity}}(n) = \frac{{\sum\nolimits_{{i \in n}} {\sum\nolimits_{{j \in n\;\{ i\} }} {\text{d}} } {\text{ist}}_{c} (i,j)}}{{|n|(|n| - 1)}}. $$
(6)

Weight of Neighbors

Higher weights for more diverse neighbors will promote items that make the recommendation list more diverse. To include this in Eq. (5) \(w_{n}(s,n)={\text{sim}}_{s} (s,n) \times {\text{diversity}}(n)\) is used.

Weight of Candidate

The candidate item itself affects the diversity of the current session. This idea can be considered in Eq. (5) by including \(\delta (i,s)\) that is the additional contribution of item i on the current session diversity and can be calculated using Eq. (2), in which dist(ij) is the distance between item i and j and can be computed using the complement of a similarity measure.

To boost accuracy, instead of diversity(n) in the neighbor selection and the weight of neighbors strategies, one can use \({\text{sim}}_{c} (s,n)\) which is the content similarity of sessions s and n. To calculate the content similarity of two sessions, we aggregate the content of items in each session and calculate the content similarity between these two aggregated vectors. For the third strategy, i.e., weight of candidate, \({\text{sim}}_{c} (i,j)\) should be substituted with dist(ij).

Balancing Diversity and Accuracy

Following the aforementioned diversity and accuracy boosting approaches, enhancing one of them degrades the other one. The desired situation is to enhance both accuracy and diversity of recommendations at the same time. This is achievable by selecting approaches based on the current session diversity. If the current session has low diversity, it implies that the user prefers more focused items, and if the current session has high diversity, it implies that the user prefers broad content. Using this argument, one can select a strategy to enhance diversity or accuracy based on the diversity of the current session. The threshold, based on which one can decide to select between boosting diversity or accuracy, is a hyper-parameter and is different across datasets. To set this threshold, one can use the diversities of previous sessions and then set the threshold as the xth percentile of the diversities of these sessions. If the diversity of the current session (test session) is higher than the threshold, the diversification approach will be applied; otherwise, the accuracy boosting approach will be selected.

Greedy Re-ranking Approach

A greedy re-ranking approach is frequently used in the literature because of its simplicity and flexibility. It can be applied on the output of any baseline RS to greedily maximize an arbitrary objective function. This function (f(iR)) can be adapted to enhance several objectives. In this paper, the objectives are relevance and diversity. Therefore, the objective function is defined as:

$$ f(i,R) = \lambda \cdot ~{\text{relevance}}(i) + (1 - \lambda )~ \cdot {\text{diversity}}(i,R). $$
(7)

In Eq. (7), relevance(i) is the relevance score of item i, diversity(iR) is the average pairwise distance of item i with items in list R, and \(\lambda \) is a balancing parameter. A greedy re-ranking algorithm starts with an initial recommendation list (S) generated from a baseline recommender system. To maximize the objective function (f(iR)), the algorithm greedily moves items from S to R until it reaches the desired length of R.

Datasets and Experiment Set-Up

One of the applications of session-based recommenders is music recommendation to recommend the next song given the current session. To evaluate the diversity and accuracy enhancing approaches, four music datasets are used in this study which are described in Table 1. For each session, the sequence of songs in the session with their artists is given. Artists of items are used to compute the diversity of a recommendation list. The 30music [57], lastfmFootnote 1. [58] and Nowplaying [59] datasets are based on listening histories, and the Aotm dataset [60] contains playlists which are considered as sessions in this study. For all of these datasets, short sessions, i.e., sessions with less than 4 items and low frequent items, i.e., items with support of less than 2, are excluded from the datasets.

The task of SBRSs is to predict the next items in the test sessions. To split the datasets to train and test sessions, the approach by [20] is used. In this approach, the datasets are divided into five slices with same duration, and then, the sessions in the last day of each slice are considered as test sessions. In each of these test sessions, we hide the last two items and compare the predictions with these hidden items. To make predictions for the test sessions, only sessions from the training data within the same slice are used. Final performance measures are computed by averaging the performance measures over the five slices. The average performance in all of these test sessions is computed and reported in the next section.

We consider three different performance measures for evaluation: precision, recall, and diversity. Precision and recall are standard formation retrieval accuracy measures that assess the model in predicting the hidden items in the current session. Diversity returns the average intra-list distance of the recommendation list, and it shows to what extent the filter bubble phenomenon exists in the recommendation list.

There are some hyper-parameters that should be tuned in our experiments. We consider the sessions in the last day of training set in each slice as the validation set to evaluate hyper-parameter values. Then, the validation set is also used in the training phase. In SR, the only hyper-parameter is the maximum number of events between two items in a rule. In SKNN, the number of neighbors is the hyper-parameter that should be tuned.Footnote 2 The threshold for balancing diversity and accuracy is another hyper-parameter that should be tuned for each dataset. To tune this hyper-parameter, we first select the thresholds in which both precision@10 and diversity@10 are higher than the original method (i.e., SR and SKNN without adaptation) in the validation set. Then, between these selected thresholds, we select one that maximizes the combination of relative changes in accuracy and diversity, still using the validation set (Eq. 8). In Eq. (8), s(x) is the score that we want to maximize, \(P@10_{(x)}\) and \(D@10_{(x)}\) are precision and diversity of the model for a recommendation list of length 10, and \(P@10_{{{\text{original}}}}\) and \(D@10_{{{\text{original}}}}\) are precision and diversity of the original method. Here, we assume that accuracy and diversity have equal weights, but one can set different weights for them across various applications. For the same reason, we set \(\lambda \) in the re-ranking approach to 0.5 in our experiments. It is not tuned, since it depends on the application. The final hyper-parameters, which are used in the experiments, are presented in Table 2:

$$ s(x) = \frac{{(P@10_{{(x)}} - P@10_{{{\text{original}}}} )}}{{P@10_{{{\text{original}}}} }} + \frac{{(D@10_{{(x)}} - D@10_{{{\text{original}}}} )}}{{D@10_{{{\text{original}}}} }}. $$
(8)
Table 1 Datasets’ descriptions
Table 2 Hyper-parameters

Results

In this section, we show the performance of our base methods (SR and SKNN) and the proposed approaches on the four music recommender datasets. To interpret their performance, we first compare their accuracy and runtime to that of several baselines including rule-based, neighborhood-based, Markov, factorization-based, and neural network-based methods.Footnote 3 The assessed baselines are as follows:

  • SKNN: recommends next items for a session based on session neighbors [23].

  • IKNN: recommends items similar to the last item of the session [29].

  • AR: uses the co-occurrence of pairs of items in previous sessions to recommend next items.

  • SR: uses the co-occurrence of pairs of items and their positions in the session to recommend next items.

  • Markov Chains (MC): recommends next items using rules based on first-order Markov Chain.

  • Session-Based Matrix Factorization (SMF): a factorization-based SBRS that considers sequential dynamics [20].

  • Factorized Personalized Markov Chains (FPMC): uses user-item matrix factorization and Markov chains in a three-dimensional tensor factorization approach [61].

  • BPR-MF: a traditional learning to rank matrix completion method that uses BPR loss function to learn the user and item low-rank matrices [44].

  • GRU4REC: an RNN-based SBRS that uses the \(TOP1_{max}\) loss function to model user sequences [31].

  • NARM: an RNN-based SBRS that uses attention mechanism to generate recommendation [32].

  • SR-GNN: uses graph structured data to model session sequences and recommends next items based on global preference and the current focus of the session [36].

The results are shown in Table 3. We can observe that simple methods such as SKNN and SR have very competitive performance with very low computational costs compared to the more complicated methods. As SBRSs needs to deal with a very large set of items in real time, complicated methods such as neural network-based ones are often too computationally demanding and therefore less efficient for online recommendation compared to these simple methods. Therefore, these simple methods can be highly effective and fast in online recommendations. These results are also supported by other studies [20, 22,23,24].

Table 3 Comparison of SBRS baselines based on accuracy and runtime for 30music dataset

The results of applying the balancing approach introduced in “Balancing Diversity and Accuracy” using four music datasets are shown in Tables 4 (30music dataset), 5 (Aotm dataset), 6 (Lastfm dataset), and 7 (Nowplaying dataset). The results of individual strategies are also included for completeness. As introduced in “Enhancing Diversity and Accuracy in Session-based Recommender Systems”, there are three strategies to boost either accuracy or diversity. In these tables, “C” refers to adding weight of candidate, “S” refers to considering weights of neighbors, and “N” refers to adding new neighbor selection criteria strategy. As mentioned in “Enhancing Diversity and Accuracy in Session-based k-Nearest Neighbors”, these strategies can be used to either boost diversity or accuracy. “D” refers to the diversification approaches, “A” refers to the accuracy boosting approaches, and “DA” refers to the balancing approach. “DA” strategy in SKNN includes all three strategies, i.e., “CSN”. Finally “_Re” refers to the re-ranking approach.

Table 4 Results for 30music dataset

As is shown in Table 4 (30music dataset), all of the proposed diversity enhancing strategies indeed improve diversity and all of the proposed accuracy enhancing strategies improve accuracy compared to the original method. In this dataset, the SR family produces more accurate and more diverse recommendations. For the SKNN method, SKNN(D : S) is the most effective strategy in enhancing diversity and SKNN(A : C) is the most effective strategy in enhancing accuracy from the three (C, N, and S) individual components. For enhancing diversity, combining the three components (CSN) results in the best diversity value, but for enhancing accuracy, the combination has similar performance with C strategy. The balancing approach successfully enhances both the accuracy and diversity of SKNN and SR. The re-ranking approach has a big impact on diversity but negative impact on accuracy measures. It is a simple approach, but it needs more time for post-filtering the recommendation list.

Table 5 (Aotm dataset) confirms the ability of diversity enhancing strategies to improve the diversity and to some extent accuracy enhancing strategies to improve accuracy of the original methods. Interestingly, SKNN(D : C) can improve diversity without deteriorating accuracy compared to the original method. The SKNN family produces higher accuracy compared to the SR family. In the SKNN family methods, S is again the most effective strategy in enhancing diversity and C is the most effective strategy in enhancing accuracy. As for the previous dataset, the balancing approach in both methods (SR and SKNN) can improve accuracy and diversity compared to the original method. The re-ranking approach again improves the diversity at the cost of reduced accuracy.

Table 5 Results for Aotm dataset

According to Table 6 (Lastfm dataset), diversity enhancing strategies can improve diversity measure compared to the original methods. The SR family has higher accuracy compared to the SKNN. In the SKNN family methods, SKNN(D : S) and SKNN(A : C) are the most effective strategies from the three (C, N, and D) individual components in enhancing diversity and accuracy. The balancing approach again is able to enhance accuracy and diversity compared to the original SR and SKNN. As before, the re-ranking approach enhances diversity, but it degrades the accuracy measures.

Table 6 Results for lastfm dataset

As Table 7 (Nowplaying dataset) shows, all of the proposed diversity boosting approaches improve diversity measure. The SKNN family produces more accurate recommendations, but the SR family has higher diversity. In the SKNN family, S is the most effective strategy in enhancing diversity and C is the most effective strategy in enhancing accuracy. For enhancing diversity and accuracy in SKNN, combining the three components (CSN) results in the best diversity and accuracy respectively. The SKNN balancing approach is able to generate more accurate and more diverse recommendation compared to the original SKNN, but this is not true for SR. SR(DA) produces more accurate recommendations but with less diversity. For the re-ranking approach, the same conclusions as before hold.

Table 7 Results for nowplaying dataset

The balancing (DA) and re-ranking (Re) approaches have different effects on the average time for generating a recommendation list. The re-ranking approach needs on average 55% more time compared to the original approaches to generate a final recommendation list. On the other hand, the balancing approaches do not have considerable effect (around 6%) on the average time of generating a recommendation list. We conclude that the balancing approaches improve the performance w.r.t. diversity and accuracy, with only a very small impact on computational cost.

Discussion

As is shown in the previous section, SKNN and SR have promising performance w.r.t. accuracy and computational cost compared to the other complicated baselines. Low computational cost is crucial in SBRSs as these RSs should be highly agile in processing the new coming sessions and recommending online. Moreover, these models are flexible in boosting accuracy and diversity. Diversification is essential in RSs to widen the range of provided information/items to avoid redundancy in recommendations and burst the filter bubbles.

The results in “Results” show that the diversity enhancing strategies and accuracy enhancing strategies (mainly A : C) in all four music datasets indeed enhance the corresponding performance measure, but, in most cases, these strategies also slightly deteriorate the other measure. Diversity and accuracy boosting approaches are able to improve performance, while their runtimes are almost the same as the original models. On the other hand, the re-ranking approach has a considerable impact on diversity, but its runtime is much higher than the original model and it degrades accuracy. This longer runtime is more concerned in SBRSs than in other RSs as they only depend on the current session. In other RSs, re-ranked lists can be generated based on user profiles and stored offline, but SBRSs should use the current sequence of visited items to generate recommendation lists online.

SR is a simple method and has shorter runtime compared to SKNN. It is only based on frequent item pairs in training sessions, and therefore, it is not too flexible in adding diversity or accuracy enhancing components. On the other hand, SKNN is based on neighbor sessions, and therefore, there are more opportunities to add these components. The weight of neighbors strategy (D : S) has the highest impact on diversity and weight of candidate strategy (A : C) has the highest impact on accuracy in all four music datasets compared to the three (C, S, and N) individual components.

The goal of balancing approach (DA) is to personalize the level of diversification based on the current session diversity. The focused users prefer items that are more related to their history and users with broad interests would prefer a wider range of items in their recommendation lists. The results in “Balancing Diversity and Accuracy” confirm that the balancing approach improves both accuracy and diversity compared to the original methods. The improvement over accuracy and diversity of the original methods indicates that the balancing approach can enhance the overall performance of the platform across all users.

In this study, we used four music datasets in which only artists of items are given as the item metadata. The proposed diversity and accuracy enhancing approaches in this study can be applied with other types of metadata such as genres, tags, and lyrics. Then, a suitable similarity measure should be selected based on the type of metadata to calculate similarities or dissimilarities between items. The proposed strategies and approaches can also be applied to other domains where the item metadata is given.

Conclusion

The filter bubble phenomenon is a pitfall in personalized environments. It limits the user experience to a small part of available content. Diversification is a way to address the filter bubble problem. While diversification is well studied in general Recommendation Systems (RSs), it has received far less attention in the context of Session-Based Recommendation Systems (SBRSs). The aim of this study was to enhance diversity in SBRSs while avoiding drop in accuracy. Two SBRSs were assessed in this study, namely Sequential Rule mining (SR) and Session-based k-Nearest Neighbor (SKNN). To enhance diversity (resp., accuracy) in SR, we considered more weights for items that have higher impact on the diversity (resp., content similarity) of the current session. SKNN is a neighborhood method that uses sessions with similar items to the current session to predict the next item. Three strategies are introduced to enhance diversity and accuracy of SKNN: neighbor selection, weight of neighbors, and weight of candidate.

In this article, we empirically compared all these diversity and accuracy enhancing methods on four music recommendation datasets. Our results showed that these performance enhancing strategies indeed improve diversity or accuracy, but deteriorate the other one. To decide upon the diversification or accuracy boosting approaches, we proposed to use the current session diversity, to personalize the adaptation. If diversity of the current session is higher than a threshold (a hyper-parameter), the corresponding user is interested in broad content and diversification is more effective. Otherwise, the user is interested in more focused content and accuracy boosting strategies have more impact on overall performance. The results of our empirical evaluations confirmed this hypothesis, as the overall performance, i.e., both accuracy and diversity, was improved over the original methods.

For future work, we propose to investigate diversity enhancing approach in model-based SBRS such as GRU4REC [30], CHAMELEON [62], and Session-based Matrix Factorization (SMF) [20]. In this study, we only assessed music datasets. Further study is needed on datasets from other domains with different types of metadata such as tags, text, and named entities in news recommendations [63] or genres, actors, and actresses in movie recommendations to evaluate the impact of the introduced methods on diversity and accuracy. Another interesting future work is to investigate the effect of using more graded feedback such as listening time and interaction timestamp [64] instead of binary implicit feedback in session-based music recommendations.

Notes

  1. 1.

    http://www.last.fm

  2. 2.

    For the SR hyper-parameter, we used values in [1,15] and for SKNN values in [20,150].

  3. 3.

    The hyper-parameters of these baselines are tuned using a validation set.

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Funding

This work was executed within the imec.icon project NewsButler. The NewsButler project is co-financed by imec and received project support from Flanders Innovation and Entrepreneurship (project no. HBC.2017.0628).

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Correspondence to Alireza Gharahighehi.

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This article is part of the topical collection “Advanced Theories and Algorithms for Next-generation Recommender Systems” guest edited by Shoujin Wang, Lin Xiao, Marko Tkalcic and Julian McAuley.

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Gharahighehi, A., Vens, C. Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems. SN COMPUT. SCI. 2, 39 (2021). https://doi.org/10.1007/s42979-020-00399-2

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Keywords

  • Session-based recommenders
  • Diversity
  • Session-based k-nearest neighbor
  • Sequential rule mining
  • Filter bubble phenomenon