AAAI Publications, The Thirtieth International Flairs Conference

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Exploiting Reviews to Generate Personalized and Justified Recommendations to Guide Users' Selections
Nevena Dragovic, Maria Soledad Pera

Last modified: 2017-05-08

Abstract


We introduce RUS, a recommender that assists users by providing personalized and justified suggestions to facilitate the task of deciding which items, among the recommended ones, are best tailored towards their individual interests. We exploit users' reviews and matrix factorization to generate recommendations that include reviewers' opinions related to item characteristics that each individual user frequently mentions. To demonstrate the validity of $RUS$ we use the Amazon dataset.

Keywords


Recommendation Systems; Information Extraction; Explanations; Summarization; Ranking; Clustering

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