摘要： 传统的协同过滤算法只根据用户对资源的评分单方面地挖掘用户兴趣,未能对用户兴趣进行划分,忽略了用户兴趣随时间推移发生的变化,影响了推荐质量。为解决上述问题,提出一种能适应用户兴趣变化和有效挖掘用户兴趣的推荐算法。在传统协同过滤算法基础上考虑了标签对推荐的影响,通过标签聚类将用户的兴趣进行划分,并在标签和用户评分2个方面对目标用户的相似用户进行二重选择。考虑到用户可能会因时间的推移发生兴趣变化,在计算标签和评分权重时融入了时间因子,以对其在时间维度上进行修正。实验结果表明,改进后的算法能更好地挖掘用户兴趣,适应用户的兴趣变化,提高推荐精度。
关键词:
推荐系统,
协同过滤,
标签聚类,
兴趣划分,
用户兴趣
Abstract: The traditional Collaborative Filtering(CF) algorithm mines user’s interest unilaterally according to scoring of resources,none of dividing the user’s interests and considering the user’s interest in time,and these affect the quality of recommendation.In order to solve the above problems,this paper proposes a recommendation algorithm which adapts to changes in user interests and exploits user interests effectively.The algorithm considers the effect of tags based on the traditional CF algorithm and divides the user interest by tags clustering and does the dual choices of similar users in the two aspects of the tags and scoring of resources.Considering change of user interest in the passage of time,the paper puts the time factor into the calculation of the weight of the tags and the scores in order to modify on the time dimension.Experimental results show that the improved algorithm can mine user better and adapt to changes in the user’s interest and improve the accuracy of the recommendation.
Key words:
recommendation system,
Collaborative Filtering(CF),
tag clustering,
interest division,
user interest
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