基于标签信息特征相似性的协同过滤个性化推荐

标签推荐系统旨在利用标签数据为用户提供个性化推荐。已有的基于标签的推荐方法往往忽视了用户和资源本身的特征,而且在相似性度量时仅针对项目相似性或用户相似性进行计算,并未充分考虑二者之间的有效融合,推荐结果的准确性较低。为了解决上述问题,将标签信息融入到结合用户相似性和项目相似性的协同过滤中,提出融合标签特征与相似性的协同过滤个性化推荐方法。该方法在充分考虑用户、项目以及标签信息的基础上,利用二维矩阵来定义用户-标签以及标签-项目之间的行为。构建用户和项目的标签特征表示,通过基于标签特征的相似性度量方法计算用户相似性和项目相似性。基于用户标签行为和用户与项目的相似性线性组合来预测用户对项目的偏好值,并根据预测偏好值排序,生成最终的推荐列表。在Last.fm数据集上的实验结果表明,该方法能够提高推荐的准确度,满足用户的个性化需求。

Collaborative Filtering Personalized Recommendation Based on Similarity of Tag Information Feature

Tag recommendation systems are aimed to provide personalized recommendation using tag data for users.Previous tag based recommendation methods usually neglect the characteristics of users and items,and similarity mea-sures are unconsidered fully incorporating effectively both user similarity and item similarity,which leads to deviation of recommendation results.To address this issue,this paper proposed the collaborative filtering recommendation method of combining tag features and similarity for personalized recommendation.Two-dimensional matrix is used to define actions among user-tag and tag-item based on integrating information among users,tags and items.Tag features representation is constructed,and user similarity and item similarity are calculated by similarity measure method based on tag features.The user preferences for items are predicted by their tag behaviors and linear combination of similarity of users and items,and the recommended list is generated according to the rank of preferences.The experimental results on Last.fm show that the proposed method can improve recommendation accuracy and satisfy the requirement for users.