| 摘要: |
| 推荐系统作为缓解信息过载问题的有效方法之一,在社交媒体中的作用日趋重要.但是,新项目冷启动和新用户冷启动问题是推荐技术面临的难题.为了解决新项目冷启动问题,提出了用户时间权重信息概念,该定义考虑到了用户评价时间与项目发布时间的时间间隔,根据用户时间权重值的大小,可以判断该用户是积极用户还是消极用户,以及用户对新项目的偏爱程度;利用三分图的形式来描述用户-项目-标签、用户-项目-属性之间的关系.在充分考虑用户、标签、项目属性、时间等信息基础上,获得个性化的预测评分值公式,提出了推荐算法.实验结果表明:所提出的方法能够实现满足不同用户、不同偏好的个性化推荐,在为用户推荐到合适项目的同时还能带来惊喜.比较实验说明,所提出的方法推荐准确度高,推荐新颖度高.交叉验证实验结果表明:该方法在解决推荐算法中的新项目冷启动问题上,无论是在推荐的准确度还是推荐项目的新颖度上都是有效的. |
| 关键词: 推荐系统 协同过滤 冷启动 个性化 标签 |
| DOI：10.13328/j.cnki.jos.004587 |
| 分类号: |
| 基金项目:国家自然科学基金(61379114, 61272060); 重庆市自然科学基金(cstc2011jjA40045) |
|
| Algorithm to Solve the Cold-Start Problem in New Item Recommendations |
|
YU Hong1,2, LI Jun-Hua1,2
|
|
1.Chongqing Key Laboratory of Computational Intelligence (Chongqing University of Posts &2.Telecommunications), Chongqing 400065, China
|
| Abstract: |
| As one of the effective methods to ease the information overload problem, recommender systems have become extremely popular in social media. However, recommender methods suffer from the cold-start problems in new item recommendations and new user recommendations. To combat the cold-start problems in new item recommendations, the concept of user time weights is proposed to characterize the time interval between the user evaluating time and item distributing time. According to the weights, it can determine whether the user is a positive user or a negative user, and the degree of the user's preference for new items. Tripartite graphs are used to picture relations among user-item-tag, and user-item-attribute. Combing information among users, tags, attributes of items and time weights, functions for predicting the rating are defined and a new personalized recommendation algorithm is constructed. Overall experimental results show that the proposed method not only brings satisfied personalized items but also pleasantly surprises different users with different preferences. Comparative experiments illustrate the proposed method is much higher in accuracy and novelty. Cross-validation experiments demonstrate that the new method is effective to solve the cold-start problem in new item recommendations. |
| Key words: recommender system collaborative filtering cold-start personalization tag |
