Spatio-temporal models for estimating click-through rate
Proceedings of the 18th international conference on World wide web
Finding trending local topics in search queries for personalization of a recommendation system
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal web dynamics and its application to information retrieval
Proceedings of the sixth ACM international conference on Web search and data mining
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User interaction plays a vital role in recommender systems. Previous studies on algorithmic recommender systems have mainly focused on modeling techniques and feature development. Traditionally, implicit user feedback or explicit user ratings on the recommended items form the basis for designing and training of recommendation algorithms. But user interactions in real-world Web applications (e.g., a portal website with different recommendation modules in the interface) are unlikely to be as ideal as those assumed by previously proposed models. To address this problem, we build an online learning framework for personalized recommendation. We argue that appropriate user action interpretation is critical for a recommender system. The main contribution in this paper is an approach of interpreting users' actions for the online learning to achieve better item relevance estimation. Our experiments on the large-scale data from a commercial Web recommender system demonstrate significant improvement in terms of a precision metric over the baseline model that does not incorporate user action interpretation. The efficacy of this new algorithm is also proved by the online test results on real user traffic.