Abstract
This paper adopts a Bayesian approach for finding top recommendations. The approach is entirely personalized, and consists of learning a utility function over user preferences via employing a sampling-based, non-intrusive preference elicitation framework. We explicitly model the uncertainty over the utility function and learn it through passive user feedback, provided in the form of clicks on previously recommended items. The utility function is a linear combination of weighted features, and beliefs are maintained using a Markov Chain Monte Carlo algorithm. Our approach overcomes the problem of having conflicting user constraints by identifying a convex region within a user’s preferences model. Additionally, it handles situations where not enough data about the user is available, by exploiting the information from clusters of (feature) weight vectors created by observing other users’ behavior. We evaluate our system’s performance by applying it in the online hotel booking recommendations domain using a real-world dataset, with very encouraging results.
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Notes
- 1.
- 2.
To illustrate, assume a “hotel price” is “low”, say 0.1. If the user prefers really cheap hotels, she might have a weight of \(-0.9\) for “hotel price”, thus deriving a higher utility for this hotel compared to that derived for an expensive hotel of price, say, 0.9 (since \(-0.09 > -0.81\)).
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Moreover, all clicked items are originally equally appealing. However, as interactions with the system increase, beliefs about the desirability of the items get updated.
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We note that ours is essentially a standard version of the Metropolis-Hastings algorithm, which is known to be almost always convergent [21].
- 5.
In some detail, we divide each of the m dimensions into a fixed number of “segments”–ten (10) in our implementation—and use this segmentation to generate “buckets” to place our samples into. In this way, we create \(10^m\) buckets in total: for instance, if we had only two dimensions, e.g. “price” and “distance to city center”, we would be creating 100 buckets. Then, each prior sample is allocated to its corresponding bucket, based on Euclidean distance. When Algorithm 1 picks a sample, it checks which bucket it belongs to, and uses the number of samples in the buckets to estimate the \(\pi (\cdot )\) density in Eq. 7. Thus, this method uses the prior samples to estimate the posterior joint density.
- 6.
Specifically, 2 out of 7 items presented to the user are chosen randomly; see Sect. 4.2 below.
- 7.
The idea of employing clustering to address the “cold start” problem has also appeared in [26]. However, that work uses averaging over user ratings to produce recommendations that are appropriate for each cluster. In our work, we make no use of user ratings over items, and make recommendations based on the clusters’ centroids rather than employing some averaging-over-cluster-contents process.
- 8.
Note that “clients’ rating” is just an item’s (a hotel’s) feature. We stress that we do not ask our system’s users to rate the items, and it is not the system’s aim to produce recommendations based on such ratings.
- 9.
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Acknowledgements
The authors would like to thank Professor Michail Lagoudakis for extremely useful suggestions for improving an earlier version of this work.
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Papilaris, MA., Chalkiadakis, G. (2019). Markov Chain Monte Carlo for Effective Personalized Recommendations. In: Slavkovik, M. (eds) Multi-Agent Systems. EUMAS 2018. Lecture Notes in Computer Science(), vol 11450. Springer, Cham. https://doi.org/10.1007/978-3-030-14174-5_13
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