Feature-Driven Interactive Recommendations and Explanations with Collaborative Filtering Approach

Naveed, S., & Ziegler, J. (to appear). ComplexRec ’19: Proceedings of the 3rd Workshop on Recommendations in complex scenarios.

Abstract

Recommender systems (RS) based on collaborative filtering (CF) or content-based filtering (CB) have been shown to be effective means to identify items that are potentially of interest to a user, by mostly exploiting user’s explicit or implicit feedback on items. Even though, these techniques achieve high accuracy in recommending, they have their own shortcomings- so hybrid solutions combining the two techniques, have emerged to overcome their disadvantages and benefit from their strengths. Another general problem can be seen in the lack of transparency of contemporary RS, where the user preference model and the recommendations that represents that model are neither explained to the current user nor the user can influence the recommendation process except for rating or re-rating (more) items. In this paper, we first enhanced the CF approach by modelling user preferences based on items’ features in a complex product domain. The user-feature model is then used as an input to the user-based CF to generate recommendations and explanations. With our proposed approach, we aim to increase transparency and offer richer interaction possibilities in current Recommender Systems- where users are allowed to express their interests in terms of features and interactively manipulate their recommendations through existing user profile and explanations.

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