PROJECT TITLE :
Exploring Hierarchical Structures for Recommender Systems - 2018
Items in real-world recommender systems exhibit bound hierarchical structures. Similarly, user preferences additionally present hierarchical structures. Recent studies show that incorporating the hierarchy of items or user preferences can improve the performance of recommender systems. However, hierarchical structures are often not explicitly on the market, especially those of user preferences. Thus, there is a gap between the importance of hierarchies and their availability. In this Project, we investigate the matter of exploring the implicit hierarchical structures for recommender systems once they aren't explicitly available. We propose a novel recommendation framework to bridge the gap, which allows us to explore the implicit hierarchies of users and items simultaneously. We then extend the framework to integrate explicit hierarchies after they are available, which provides a unified framework for each express and implicit hierarchical structures. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework by incorporating implicit and explicit structures.
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