基于大数据集的混合动态协同过滤算法研究

摘要

Collaborative filtering has been widely used in the recommendation system , but the traditional algorithm has some limitations , such as inability to adapt to the sparsity of user-item rating matrix data sets well, failure to consider the classification of item , users'scores, interest change over time and other factors when calculating the similarity of the items .Regarding these limitations , it proposed a big data set hybrid dynamic collaborative filtering algorithm , based on the traditional collaborative filtering algorithm . When calculating the similarity of items , time decay functions were introduced in the algorithm , which considered both the similarity of items , scores and items classified .The weights of project integrated simi-larity could be adjusted automatically .In the algorithm , some improvements have also been made in simi-larity computing and the selection of the neighboring items .To verify the effectiveness of the algorithm , experiments were done on movie-lens data sets .Experimental results show that the algorithm is better than the traditional recommendation algorithms .%协同过滤已在推荐系统中广泛使用，但传统算法存在一定的局限性，如不能较好地适应用户-项目评分矩阵数据集的稀疏性、计算项目相似性时未考虑项目的分类及用户对项目评分和兴趣的时变性等因素。针对这些局限性在传统协同过滤算法基础上提出一种基于大数据集的混合动态协同过滤算法。该算法在计算项目的相似性时引入了时间衰减函数，并综合考虑项目评分的相似性和项目分类的相似性，两者在项目综合相似性中所占权重可以自适应动态调节。算法还在相似性计算和近邻项目选取上做了一些改进。实验表明该算法比传统推荐算法质量有所提高。