ABSTRACT
Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics.
- }}Amatriain, X., Pujol, J., Tintarev, N., and Oliver, N. Rate it again: increasing recommendation accuracy by user rerating. Proceedings of the third ACM conference on Recommender systems, ACM (2009), 173--180. Google ScholarDigital Library
- }}Amatriain, X., Pujol, J., and Oliver, N. I like it... I like it not: Evaluating User Ratings Noise in Recommender Systems. In G. Houben, G. McCalla, F. Pianesi and M. Zancanaro, User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, 247--258. Google ScholarDigital Library
- }}Anand, S. S., Kearney, P., and Shapcott, M. Generating semantically enriched user profiles for Web personalization. ACM Transactions on Internet Technology 7, 4 (2007), 22--es. Google ScholarDigital Library
- }}Herrada, C. Music recommendation and discovery in the long tail. 2008.Google Scholar
- }}Hu, Y., Koren, Y., and Volinsky, C. Collaborative Filtering for Implicit Feedback Datasets. 2008 Eighth IEEE International Conference on Data Mining, (2008), 263--272. Google ScholarDigital Library
- }}Jawaheer, G., Szomszor, M., and Kostkova, P. Characterisation of explicit feedback in an online music recommendation service. ACM Recommender Systems Conference 2010, Barcelona (in press), (2010). Google ScholarDigital Library
- }}Kelly, D. and Teevan, J. Implicit feedback for inferring user preference: a bibliography. ACM SIGIR Forum, (2003). Google ScholarDigital Library
- }}Teevan, J., Dumais, S., Horvitz, E., and others. Potential for Personalization. ACM Transactions on Computer-Human Interaction 1, 212 (2008), 1--35. Google ScholarDigital Library
- }}White, R., Jose, J., and Ruthven, I. Comparing explicit and implicit feedback techniques for web retrieval: Trec-10 interactive track report. NIST SPECIAL PUBLICATION SP, (2002), 534--538.Google Scholar
- }}Williamson, S. and Ghahramani, Z. Probabilistic models for data combination in recommender systems. NIPS 2008 Workshop:, (2008), 1--4.Google Scholar
Index Terms
- Comparison of implicit and explicit feedback from an online music recommendation service
Recommendations
Unifying explicit and implicit feedback for collaborative filtering
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge managementMost collaborative filtering algorithms are based on certain statistical models of user interests built from either explicit feedback (eg: ratings, votes) or implicit feedback (eg: clicks, purchases). Explicit feedbacks are more precise but more ...
Characterisation of explicit feedback in an online music recommendation service
RecSys '10: Proceedings of the fourth ACM conference on Recommender systemsIn this paper, we present our study and characterisation of explicit and implicit feedback on Last.fm, an online music station and recommender service. The dataset consisted of explicit positive feedback (through loved tracks) and implicit positive ...
A time-based approach to effective recommender systems using implicit feedback
Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations using explicit ratings on items from users. In some e-commerce environments, ...
Comments