Although the current Recommender Systems (RSs) focus on discovering unknown items for users in several domains, users may be particularly interested in consuming items in which they are already familiar. As a result, this study aims to uncover subsets of known items that are useful for recommendations in the present. The main argument highlighted in this study is that past consumption is a rich source of relevant recommendations neglected or underexploited by current RSs. Thus, we propose a methodology to quantify the effectiveness of recommending known items in real domains. Afterwards, we proposed distinct heuristics to search the consumption history of each user items unexpected to be consumed, but potentially relevant. Such heuristics exploit time-related, context-related, and relevance-related information; as well as a combination of these three types of information. Assessments on real collections allowed us to verify the applicability of our methodology. Furthermore, offline evaluations demonstrated that past relevance, consumption recency, and associations with currently consumed items are useful information to model reconsumption. Finally, through a user study with members of Movielens, we verified that users are willing to reconsume some known items and recognized the value in this type of recommendation. Therefore, by exploiting the long history of each user, we are able to match a piece of the user's preference neglected by current RSs, hence, improving the user's satisfaction.
Bibliographical noteFunding Information:
This work is partially supported by CAPES , CNPq , Finep , Fapemig , MasWeb and InWeb.
© 2017 Elsevier Ltd
Copyright 2017 Elsevier B.V., All rights reserved.
- User modeling