A Personalized Interest-Forgetting Markov Model for Recommendations
A Personalized Interest-Forgetting Markov Model for Recommendations
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DOI：
10.1609/aaai.v29i1.9165
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发表时间：
2015-01
影响因子：
1.8
通讯作者：
Jun Chen;Chaokun Wang;Jianmin Wang
中科院分区：
文献类型：
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作者：
Jun Chen;Chaokun Wang;Jianmin Wang
智能项目推荐是人工智能研究中的一个关键问题，它使推荐系统在生成推荐时更具“人性化”。然而，人类的一个主要特征-遗忘，几乎没有被讨论的推荐系统。针对个性化推荐中存在的兴趣遗忘问题，提出了一种将兴趣遗忘特性与马尔可夫模型相结合的个性化推荐框架。该框架的多个实现进行了调查和比较。实验结果表明，该方法能显著提高项目推荐的准确率，验证了在推荐中考虑兴趣遗忘的重要性。
Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.