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A random-walk based recommendation algorithm considering item categories
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AI
DOI：10.1016/j.neucom.2012.06.062.png)
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Recommender systems aim at recommending information items or social elements that are likely to be of interest to users. In this paper, we propose a recommendation algorithm which takes into account user's preference on item categories, and computes rank scores in different categories for each item, in order to make suggestions based on both user's previous interactions and item contents. By considering item categories and user preference, we are able to avoid the dominance of some popular items. Empirical experiments on MovieLens dataset demonstrate that the algorithm outperforms other state-of-the-art recommendation algorithms. (c) 2013 Elsevier B.V. All rights reserved.
Keyword:
Recommender system
Random-walk
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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引用论文
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Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions走向下一代推荐系统: 对最新技术和可能扩展的调查
PREAI
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