Abstract

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 …

Date

2017

Authors

Yizhou Sun, Hongzhi Yin, Xiang Ren

Book

Proceedings of the 26th International Conference on World Wide Web Companion

Pages

941-945