在推荐系统中利用时间因素的方法

Method by using time factors in recommender system

针对传统推荐算法忽略时间因素的问题,根据个体用户短期行为的相似性,利用时间衰减函数计算项目间相关关系,提出基于用户兴趣的项目关联度;将其用于项目相似度的计算,提出基于用户兴趣的项目相似度;同时基于项目关联度对ItemRank算法进行改进,提出一种结合时间因素的TItemRank算法。实验结果表明,利用项目关联度对推荐算法进行改进时,在推荐项目数较少的情况下能够明显地改善推荐效果。特别地,在推荐项目数为20时,基于用户兴趣的项目相似度相比余弦相似度和Jaccard相似度,推荐准确率分别提高了21.9%、6.7%;在推荐项目数为5时,TItemRank算法相比ItemRank算法推荐准确率提高2.9%。

Concerning the problem that traditional recommendation algorithm ignores the time factors, according to the similarity of individuals＇short-term behavior, a calculation method of item correlation by using time decay function based on users＇interest was proposed. And based on this method, a new item similarity was proposed. At the same time, the TItemRank algorithm was proposed which is an improved ItemRank algorithm by combining with the user interest-based item correlation. The experimental results show that： the improved algorithms have better recommendation effects than classical ones when the recommendation list is small. Especially, when the recommendation list has 20 items, the precision of user interest-based item similarity is 21. 9% higher than Cosin similarity and 6. 7% higher than Jaccard similarity. Meanwhile,when the recommendation list has 5 items, the precision of TItemRank is 2. 9% higher than ItemRank.