Mohammad Aliannejadi
Ida Mele
Fabio Crestani
ABSTRACT
Suggesting personalized venues helps users to find interesting places on location-based social networks (LBSNs). Although there are many LBSNs online, none of them is known to have thorough information about all venues. The Contextual Suggestion track at TREC aimed at providing a collection consisting of places as well as user context to enable researchers to examine and compare different approaches, under the same evaluation setting. However, the officially released collection of the track did not meet many participants' needs related to venue content, online reviews, and user context. That is why almost all successful systems chose to crawl information from different LBSNs. For example, one of the best proposed systems in the TREC 2016 Contextual Suggestion track crawled data from multiple LBSNs and enriched it with venue-context appropriateness ratings, collected using a crowdsourcing platform. Such collection enabled the system to better predict a venue's appropriateness to a given user's context. In this paper, we release both collections that were used by the system above. We believe that these datasets give other researchers the opportunity to compare their approaches with the top systems in the track. Also, it provides the opportunity to explore different methods to predicting contextually appropriate venues.
- Mohammad Aliannejadi and Fabio Crestani 2017. Venue Appropriateness Prediction for Personalized Context-Aware Venue Suggestion SIGIR 2017. ACM.Google Scholar
- Mohammad Aliannejadi, Ida Mele, and Fabio Crestani. 2016. User Model Enrichment for Venue Recommendation. In AIRS 2016. Springer, 212--223. Google ScholarDigital Library
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- Peilin Yang, Hongning Wang, Hui Fang, and Deng Cai. 2015. Opinions matter: a general approach to user profile modeling for contextual suggestion. Information Retrieval Journal Vol. 18, 6 (2015), 586--610. Google ScholarDigital Library
Index Terms
- A Cross-Platform Collection for Contextual Suggestion
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