项目子相似度融合的协同过滤推荐算法

针对用户评分数据稀疏性和项目最近邻寻找的不准确性问题,提出了一种项目子相似度融合的协同过滤推荐算法.该算法根据目标用户每一属性取值,选取与该属性值一致的用户作为用户子空间,并在此空间上计算目标项目与其他项目之间的相似度(称其为项目子相似度).在此基础上,以项目子相似度为依据选取目标项目的K最近邻,计算其预测评分;最后对用户不同属性上的预测评分进行加权求和,得到目标项目的最终评分.实验结果表明,该算法能准确地选取目标项目的最近邻,明显改善了推荐质量.

Collaboration Filtering Recommendation Algorithm of Sub-Similarity Integration between Items

Aiming at such the problems of sparse data and non-currency to select the nearest neighbors, a collaborative filtering recommendation algorithm of sub-similarity integration between items is proposed in the paper. According to every attribute value of the target user, the users whose attribute value is the same as target user's are selected as user's sub-space, similarity(sub-similarity of items) between the target item and others in the user's sum-space is calculated. Based on it, according to sub-similarity of items, k-nearest-neighbors are selected to calculate it's prediction value. Finally, weighted sum of prediction value of user's attributes is calculated to get final prediction value of the target item. Experimental result shows that the algorithm can select nearest neighbors of target item correctly and improve recommendation quality of spare data.