An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering

TL;DR

This paper introduces an adaptive hybrid active learning approach with free ratings for collaborative filtering, significantly improving recommendation accuracy by intelligently selecting informative ratings and leveraging item side information.

Contribution

It proposes a novel hybrid active learning strategy that combines personalized and non-personalized methods, along with a new free ratings procedure based on item side information.

Findings

Hybrid strategy outperforms existing methods

Free ratings enhance recommendation performance

Improved user experience with fewer ratings needed

Abstract

Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to model the behavior of users and to generate recommendations. Typically, the ratings are quite sparse, i.e., only a small fraction of items are rated by each user. To address this issue and enhance the performance, active learning strategies can be used to select the most informative items to be rated. This rating elicitation procedure enriches the interaction matrix with informative ratings and therefore assists the recommender system to better model the preferences of the users. In this paper, we evaluate various non-personalized and personalized rating elicitation strategies. We also propose a hybrid strategy that adaptively combines a non-personalized and a personalized strategy. Furthermore, we propose a new procedure to obtain free ratings based on the side information of the items. We evaluate these ideas on the MovieLens dataset. The experiments reveal that our proposed hybrid strategy outperforms the strategies from the literature. We also propose the extent to which free ratings are obtained, improving further the performance and also the user experience.