Journal Title
Title of Journal: User Model UserAdap Inter
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Abbravation: User Modeling and User-Adapted Interaction
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Publisher
Springer Netherlands
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Authors: Victor Codina Francesco Ricci Luigi Ceccaroni
Publish Date: 2015/03/31
Volume: 26, Issue: 1, Pages: 1-32
Abstract
Contextaware recommender systems improve contextfree recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item They use data sets of contextuallytagged 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 prefiltering approach to contextaware recommendation called distributionalsemantics prefiltering DSPF which exploits in a novel way the distributional semantics of contextual conditions to build more precise contextaware rating prediction models In DSPF given a target contextual situation of a target user a matrixfactorization 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 situationtosituation similarity We also show how DSPF can be further improved by using clustering techniques Finally we evaluate DSPF on several contextuallytagged data sets and demonstrate that it outperforms stateoftheart contextaware approachesThe research described in this paper is partly supported by the SuperHub and the Citclops European projects FP7ICT20117 FP7ENV308469 and the Universitat Politècnica de Catalunya – BarcelonaTech UPC under an FPIUPC 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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