Revisiting the performance evaluation of knowledge-aware recommender systems: are we making progress?

P. 22–28.

Ananyeva M., Lashinin O., Kuznetsova M.

Knowledge-aware recommender systems incorporate side information to improve recommendation performance. The authors of new algorithms are usually focused on developing new ideas behind the proposed methods and comparing their models with existing knowledge-aware recommender models. Meanwhile, some commonly used state-of-the-art general top-n recommender models are ignored as potential baselines. In this study, we compare previously proposed knowledge-based recommender systems with simple and computationally effective recommender models (EASE and ItemKNN) that do not use any additional information about users and items. Our results on three datasets show that claimed effect of using side information in recommender systems is still questionable.

Keywords: evaluation, recommender systems, knowledge-based model

In book

Proceedings of the Fourth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 16th ACM Conference on Recommender Systems (RecSys 2022)

Vol. 3294. , CEUR Workshop Proceedings, 2022.