Publications
CONFERENCE (INTERNATIONAL) CERAM: Coverage Expansion for Recommendations by Associating Discarded Models
Yoshiki Matsune (Ritsumeikan University), Kota Tsubouchi, Nobuhiko Nishio (Ritsumeikan University)
28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2022)
August 22, 2022
Systems that utilize and manage predictive models have become increasingly significant in industry. In the services offered by Yahoo! JAPAN, once a predictive model is utilized for recommendations, it is thrown away. Such models could however be reused for expanding the coverage of other recommendations. Here, our goal is to construct recommendation systems that expand the coverage of recommendations by effectively utilizing models which would otherwise be discarded. Another goal is to deploy such a recommendation system on real services and make practical use of it. In this paper, we describe a recommendation system that achieves these two goals by overcoming the challenges facing its deployment on real services. Specifically, we developed an optimization method that alleviates the psychological barrier against using the recommendation system and clarified the performance of our method in making real recommendations. An offline test and a large-scale online test on making real recommendations showed that our method substantially expands the coverage of recommendations. As a highlight of the results, our method made recommendations to 76.9 times more users at the same level of recommendation performance as the currently used recommendation system by the service. Overall, the results show that our method has a huge impact on services and can be applied to real recommendations.
Paper :
CERAM: Coverage Expansion for Recommendations by Associating Discarded Models
(external link)