BJUT at TREC 2015 Contextual Suggestion Track
نویسندگان
چکیده
In this paper we described our efforts for TREC contextual suggestion task. Our goal of this year is to evaluate the effectiveness of: (1) predict user preferences of each scenic spot based on non-negtive matrix factorization, (2) automatic summarization method that leverages the information from multiple resources to generate the description for each candidate scenic spots; and (3) hybrid recommendation method that combing a variety of factors to construct a system of hybrid recommendation system. Finally, we conduct extensive experiments to evaluate the proposed framework on TREC 2015 Contextual Suggestion data set, and, as would be expected, the results demonstrate its generality and superior performance.
منابع مشابه
BJUT at TREC 2014 Contextual Suggestion Track: Hybrid Recommendation Based on Open-web Information
In this paper we describe our efforts for TREC contextual suggestion task. Our goal of this year is to evaluate the effectiveness of: (1) Preference crawling method that as far as possible to obtain more candidate spots’ information from open-web to model the users’ interest profiles; (2) Automatic summarization method that leverages the information from multiple resources to generate the descr...
متن کاملUniversity of Lugano at TREC 2015: Contextual Suggestion and Temporal Summarization Tracks
This technical report presents the work of the University of Lugano at TREC 2015 Contextual Suggestion and Temporal Summarization tracks. The first track that we report on, is the Contextual Suggestion. The goal of the Contextual Suggestion track is to develop systems that could generate user-specific suggestions that a user might potentially like. Our proposed method attempts to model the user...
متن کاملWaterlooClarke: TREC 2015 Contextual Suggestion Track
In this work we present a first attempt at developing a live system to solve the problem presented in the TREC 2015 contextual suggestion task. The goal of this task is to tailor point-of-interest suggestions to users according to their preferences [3]. We present how we gathered data for the candidate pointsof-interest, filtered some of the candidates and built a live system to return suggesti...
متن کاملUniversity of Glasgow at TREC 2015: Experiments with Terrier in Contextual Suggestion, Temporal Summarisation and Dynamic Domain Tracks
In TREC 2015, we focus on tackling the challenges posed by the Contextual Suggestion, Temporal Summarisation and Dynamic Domain tracks. For Contextual Suggestion, we investigate the use of user-generated data in location-based social networks (LBSN) to suggest venues. For Temporal Summarisation, we examine features for event summarisation that explicitly model the entities involved in the event...
متن کاملLaval University and Lakehead University Experiments at TREC 2015 Contextual Suggestion Track
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 ...
متن کامل