POI popularity prediction via hierarchical fusion of multiple social clues

Publication Type:
Conference Proceeding
SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, pp. 1001 - 1004
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© 2017 Copyright held by the owner/author(s). Predicting the popularity of Point of Interest (POI) has be-come increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarci-Ty of POI's information, which tendentiously confines the recommendation to popular scenic spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion (HMCF), for predicting the popularity of POIs. Specifically, we devise an effective hierarchy to comprehensively describe POI by integrating various types of media information (e.g., image and text) from multiple social sources. For each individual POI, we simultaneously inject se-mantic knowledge as well as multi-clue representative power. We collect a multi-source POI dataset from four widely-used tourism platforms. Extensive experimental results show that the proposed method can significantly improve the perfor-mance of predicting the attractions' popularity as compared to several baselines.
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