Laval University and Lakehead University Experiments at TREC 2015 Contextual Suggestion Track
نویسندگان
چکیده
In this paper we describe our effort on TREC Contextual Suggestion Track. We present a recommendation system that built upon ElasticSearch along with a machine learning re-ranking model. We utilize real world users’ opinion as well as other information to build user profiles. With profile information, we then construct customized ElasticSearch queries to search on various fields. After that, a learning to rank regressor is implemented to give better ranking results. Track results of our submitted runs show the effectiveness of the system. Keywords—ElasticSearch, Boosting Query, Recommendation System, Point-wise re-ranking
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تاریخ انتشار 2015