Exploiting various implicit feedback for collaborative filtering

Authors:
Byoungju Yang;Sangkeun Lee;Sungchan Park;Sang-goo Lee
Affiliations:
Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea
Venue:
Proceedings of the 21st international conference companion on World Wide Web
Year:
2012

Citing 3
Cited 1

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Collaborative Filtering for Implicit Feedback Datasets

ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A user-item relevance model for log-based collaborative filtering

ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval

A unified framework for recommending items, groups and friends in social media environment via mutual resource fusion

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

So far, many researchers have worked on recommender systems using users' implicit feedback, since it is difficult to collect explicit item preferences in most applications. Existing researches generally use a pseudo-rating matrix by adding up the number of item consumption; however, this naive approach may not capture user preferences correctly in that many other important user activities are ignored. In this paper, we show that users' diverse implicit feedbacks can be significantly used to improve recommendation accuracy. We classify various users' behaviors (e.g., search item, skip, add to playlist, etc.) into positive or negative feedback groups and construct more accurate pseudo-rating matrix. Our preliminary experimental result shows significant potential of our approach. Also, we bring out a question to the previous approaches, aggregating item usage count into ratings.