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Distributional semantic pre-filtering in context-aware recommender systems

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Abstract

Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target user would evaluate (rate) an item in a given contextual situation, with the ultimate goal to recommend the items with the best estimated ratings. This paper describes and evaluates a pre-filtering approach to context-aware recommendation, called distributional-semantics pre-filtering (DSPF), which exploits in a novel way the distributional semantics of contextual conditions to build more precise context-aware rating prediction models. In DSPF, given a target contextual situation (of a target user), a matrix-factorization predictive model is built by using the ratings tagged with the contextual situations most similar to the target one. Then, this model is used to compute rating predictions and identify recommendations for that specific target contextual situation. In the proposed approach, the definition of the similarity of contextual situations is based on the distributional semantics of their composing conditions: situations are similar if they influence the user’s ratings in a similar way. This notion of similarity has the advantage of being directly derived from the rating data; hence it does not require a context taxonomy. We analyze the effectiveness of DSPF varying the specific method used to compute the situation-to-situation similarity. We also show how DSPF can be further improved by using clustering techniques. Finally, we evaluate DSPF on several contextually-tagged data sets and demonstrate that it outperforms state-of-the-art context-aware approaches.

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Notes

  1. South Tyrol Suggest is a mobile application currently available on the Google Play Store. See [https://play.google.com/store/apps/details?id=it.unibz.sts.android] (accessed June \(4{\mathrm{th}}\), 2014).

  2. See [http://www.movielens.org] (accessed October \(27{\mathrm{th}}\), 2013).

  3. See [http://www.librarything.com] (accessed October \(27{\mathrm{th}}\), 2013)

  4. Note that this does not depend on the fact that the best semantic vector representation in each data set was selected (as described in the previous section) by using the direct measure of the similarity. In fact, the best semantic vector representation, i.e., either the user-based or the item-based (as mentioned previously) does not change if the similarity measure is changed.

  5. In Tourism we used a slightly different variant that employs Singular Value Decomposition to reduce the dimensionality of the original item-based semantic vectors because, in this particular case, it improved significantly the results. (See Codina et al. (2013b) for more details about this variant).

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Acknowledgments

The research described in this paper is partly supported by the SuperHub and the Citclops European projects (FP7-ICT-2011-7, FP7-ENV-308469), and the Universitat Politècnica de Catalunya – BarcelonaTech (UPC) under an FPI-UPC Grant. The opinions expressed in this paper are those of the authors and are not necessarily those of SuperHub or Citclops projects’ partners.

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Authors and Affiliations

  1. Software Department, Technical University of Catalonia, Girona 1-3, K2M 201, 08034, Barcelona, Spain

    Victor Codina

  2. Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, 39100, Bolzano, Italy

    Francesco Ricci

  3. 1000001 Labs, c. Alzina 52, 08024, Barcelona, Spain

    Luigi Ceccaroni

Authors
  1. Victor Codina
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  2. Francesco Ricci
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  3. Luigi Ceccaroni
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Corresponding author

Correspondence to Victor Codina.

Appendix

Appendix

The following Table 10 describes the abbreviations used to identify the considered prediction model variants.

Table 10 Description of abbreviations used to identify the evaluated model variants
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Codina, V., Ricci, F. & Ceccaroni, L. Distributional semantic pre-filtering in context-aware recommender systems. User Model User-Adap Inter 26, 1–32 (2016). https://doi.org/10.1007/s11257-015-9158-2

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