基于加权三部图的协同过滤推荐算法

针对基于用户的协同过滤算法推荐结果过度集中在热门物品,导致多样性和新颖性较低、覆盖率较小的问题,文中提出基于加权三部图的协同过滤推荐算法.在分析数据稀疏和附加信息较少的基础上引入标签信息,可同时反映用户兴趣和物品属性,利用用户、物品和标签三元关系构建三部图.通过三部图网络映射到单模网络的方法获得用户偏好度,构建用户偏好度加权的三部图模型.根据热传导方法在加权三部图上进行资源重分配,挖掘更多的相似关系,利用协同过滤框架预测评分并进行推荐.在真实数据集上的实验表明,文中算法可较好地挖掘长尾物品,实现个性化推荐.

Collaborative Filtering Recommendation Algorithm Based on Weighted Tripartite Network

The over-concentration of recommendation results of user-based collaborative filtering algorithm on popular items causes the lack of diversity, novelty and coverage. Aiming at this problem, a collaborative filtering recommendation algorithm based on weighted tripartite network is proposed. Based on sparse analysis data and little additional information, tags are introduced to reflect user interests and item attributes simultaneously. Ternary relationships among users, items and tags are utilized to construct a tripartite network.The user preference is obtained by projecting the tripartite network to the one-mode network, and a tripartite network model weighted by user preference is constructed. According to the heat spreading method, resources are redistributed on the weighted tripartite network to find more similarity relationships. The standard framework of collaborative filtering is applied for prediction and recommendation. Experiments on real datasets show that the proposed method mines long-tail items better and realizes personalized recommendations.