A Novel Recommending Algorithm Based on Topical PageRank

Authors:
Liyan Zhang;Chunping Li
Affiliations:
School of Software, Tsinghua University,;School of Software, Tsinghua University,
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

Citing 7
Cited 0

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Abstract

In this paper, we propose a Topical PageRank based algorithm for recommender systems, which ranks products by analyzing previous user-item relationships, and recommends top-rank items to potentially interested users. In order to rank all the items for each particular user, we attempt to establish a correlation graph among items, and implement ranking process with our algorithm. We evaluate our algorithm on MovieLens dataset and empirical experiments demonstrate that it outperforms other state-of-the-art recommending algorithms.