Improving User Topic Interest Profiles by Behavior Factorization
Venue
Proceedings of the 24th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2015), pp. 1406-1416
Publication Year
2015
Authors
Zhe Zhao, Zhiyuan Cheng, Lichan Hong, Ed H. Chi
BibTeX
Abstract
Many recommenders aim to provide relevant recommendations to users by building
personal topic interest profiles and then using these profiles to find interesting
contents for the user. In social media, recommender systems build user profiles by
directly combining users' topic interest signals from a wide variety of consumption
and publishing behaviors, such as social media posts they authored, commented on,
+1'd or liked. Here we propose to separately model users' topical interests that
come from these various behavioral signals in order to construct better user
profiles. Intuitively, since publishing a post requires more effort, the topic
interests coming from publishing signals should be more accurate of a user's
central interest than, say, a simple gesture such as a +1. By separating a single
user's interest profile into several behavioral profiles, we obtain better and
cleaner topic interest signals, as well as enabling topic prediction for different
types of behavior, such as topics that the user might +1 or comment on, but might
never write a post on that topic. To do this at large scales in Google+, we
employed matrix factorization techniques to model each user's behaviors as a
separate example entry in the input user-by-topic matrix. Using this technique,
which we call "behavioral factorization", we implemented and built a topic
recommender predicting user's topical interests using their actions within Google+.
We experimentally showed that we obtained better and cleaner signals than baseline
methods, and are able to more accurately predict topic interests as well as achieve
better coverage.
