Activating the Crowd: Exploiting User-Item Reciprocity for Recommendation
Larson, Martha; Said, Alan; Shi, Yue; Cremonesi, Paolo; Tikk, Domonkos; Karatzoglou, Alexandros; Baltrunas, Linas; Geurts, Jozef; Anguera, Xavi; Hopfgartner, Frank
Recommender systems have always faced the problem of sparse data. In the current era, however, with its demand for highly personalized, real-time, context-aware recommendation, the sparse data problem only threatens to grow worse. Crowd-sourcing, specifically, outsourcing micro-requests for information to the crowd, opens new possibilities to fight the sparse data challenge. In this paper, we lay out a vision for recommender systems that, instead of consulting an external crowd, rely on their own user base to actively supply the rich information needed to improve recommendations. We propose that recommender systems should create and exploit reciprocity between users and items. Specifically, recommender systems should not only recommend items for users (who would like to watch or buy them), but also recommend users for items (that need additional information in order that they can be better recommended by the system). Reciprocal recommendations provide a gentle incentivization that can be deployed non-invasively, yet is powerful enough to promote a productive symbiosis between users and items. By exploiting reciprocity, recommender systems can “look inwards” and activate their own user base to contribute the information needed to improve recommendations for the entire user community.
Published in: CrowdRec’13: Proceedings of the International Workshop on Crowdsourcing and Human Computation for Recommender Systems, Hong Kong, China,
- Published version available at http://crowdrec2013.noahlab.com.hk/papers/crowdrec2013_Larson.pdf
