Liyan Zhang
Kai Zhang
ABSTRACT
In this paper, we propose a Topical PageRank based algorithm for recommender systems, which aim to rank products by analyzing previous user-item relationships, and recommend top-rank items to potentially interested users. We evaluate our algorithm on MovieLens dataset and empirical experiments demonstrate that it outperforms other state-of-the-art recommending algorithms.
- F. Fouss, A. Pirotte, and M. Saerens. A novel way of computing similarities between nodes of a graph, with application to collaborative recommendation. In Web Intelligence, pages 550--556, 2005. Google Scholar
Digital Library
- M. Gori and A. Pucci. Research paper recommender systems: A random-walk based approach. In Web Intelligence, pages 778--781, 2006. Google ScholarDigital Library
- L. Nie, B. D. Davison, and X. Qi. Topical link analysis for web search. In SIGIR, pages 91--98, 2006. Google ScholarDigital Library
Index Terms
- A topical PageRank based algorithm for recommender systems
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