Context-Rich Recommendation: Integrating Links, Text, and Spatio-Temporal Dimensions

author: Hongzhi Yin, School of Information Technology and Electrical Engineering, University of Queensland
author: Xiang Ren, University of Illinois at Urbana-Champaign
author: Yizhou Sun, Computer Science Department, University of California, Los Angeles, UCLA
published: Nov. 21, 2017,   recorded: August 2017,   views: 1065

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Recommendation has received tremendous attention recently due to its wide and successful applications across different domains. Different from traditional setting of recommendation tasks, modern recommendation tasks are usually exposed in a context-rich environment. For example, in addition to a user-item rating matrix, users and items are connected to other objects via different relationships and they are usually associated with rich attributes, such as text and spatio-temporal information. It turns out that heterogeneous information network serves a natural data model to capture the rich context of these recommendation tasks. In this tutorial, we will systematically introduce the methodologies of using heterogeneous information network mining approach to solve recommendation tasks, and demonstrate the effectiveness of such methods using different applications, ranging from collaboration recommendation in scientific research network to job recommendation in professional social network, and to drug discovery in biomedical networks. The topics to be covered in the tutorial include: (1) overall introduction; (2) recommendation in heterogeneous information networks, which introduces the general methodology of how to model the recommendation problem as a heterogeneous information network mining problem; (3) recommendation in a text-rich setting, where the information network is further enriched by refined analysis of text information; (4) recommendation with spatio-temporal information, where entities and relationships in the network are associated with spatio-temporal attributes; and (5) research frontiers for context-rich recommendation.

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