Towards Understanding Latent Factors and User Profiles by Enhancing Matrix Factorization with Tags

Donkers, T., Loepp, B., & Ziegler, J. (2016). In Poster Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16).

Abstract

With the interactive recommending approach we have recently proposed, users are given more control over model-based Collaborative Filtering while the results are perceived as more transparent. Integrating the latent factors derived by Matrix Factorization with tags users provided for the items has, however, even more advantages. In this paper, we show how general understanding of the abstract factor space, and of user and item positions inside it, can benefit from the semantics introduced by considering additional information. Moreover, our approach allows us to explain the user’s (former latent) preference profile by means of tags.

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Research development

TagMF

Increasing transparency and offering richer interaction possibilities in today's recommender systems

Focus area