The Oblivion Problem: Exploiting forgotten items to improve recommendation diversity

Novelty and Diversity in Recommender Systems 2011.

计算机科学;

Fernando Mourão ; Claudiane Fonseca ; Camila Araújo ; Wagner Meira Jr.

Others  :  http://ceur-ws.org/Vol-816/paper4.pdf
PID  :  42537

学科分类：计算机科学（综合）

来源: CEUR

【 摘 要 】

Recommender Systems (RSs) have become a crucial tool to assist users in their choices on various commercial applications. Despite recent advances, there is still room for more effective techniques that are applicable to a larger range of domains. A major challenge recurrently researched is the lack of diversity in the recommenda- tion lists provided by current RSs. That is, besides being effec- tive to suggest interesting items to users, a good RS should provide useful and diversified items. In order to address this problem, we evaluate the use of forgotten items in recommendation. By forgot- ten items, we mean items that have been very relevant to users in the past but are not anymore. Therefore, we formally define the Oblivion Problem, which is the problem of recommending forgot- ten items, propose a methodology for verifying it in real scenarios, and perform a deep characterization of this problem in a relevant music domain, the Last.fm system. Applying our methodology to Last.fm has demonstrated the existence of the oblivion problem in practice, as well as showed the utility of this methodology. Further, the behavior exhibited by forgotten items in Last.fm suggests that defining techniques that incorporate such items into RSs consists in