计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (13): 154-156.
• 数据库、信号与信息处理 • 上一篇 下一篇
李克潮，梁正友
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LI Kechao，LIANG Zhengyou
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摘要： 协同过滤是目前推荐系统中最为成功的推荐技术，但传统的协同过滤算法没有考虑用户兴趣的变化。针对上述问题，从艾宾浩斯记忆遗忘规律得到启发，提出一种基于资源相似度的协同过滤算法，利用基于指数遗忘的数据权重来逐步减小资源相似度的权重。实验结果表明，该算法显著提高推荐系统的推荐质量。
关键词: 协同过滤, 推荐算法, 用户兴趣, 指数遗忘
Abstract: Collaborative Filtering（CF） is the most successful technologies to date，but traditional collaborative filtering algorithm does not consider the change of users’ interests.To solve the problem，this paper presents a CF recommendation algorithm，named item similarity-based CF algorithm，which uses exponential gradual forgetting-based data weight to diminish the importance of each item similarity.The experimental results show that the proposed algorithm can efficiently improve recommendation quality.
Key words: collaborative filtering, recommendation algorithm, users’ interests, exponential forgetting
李克潮，梁正友. 适应用户兴趣变化的指数遗忘协同过滤算法[J]. 计算机工程与应用, 2011, 47(13): 154-156.
LI Kechao，LIANG Zhengyou. Exponential forgetting collaborative filtering recommendation algorithm incorporated with user interest change[J]. Computer Engineering and Applications, 2011, 47(13): 154-156.
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