Abstract: To improve the accuracy of the recommendation system, a Location-Based Asymmetric Similarity for Collaborative Filtering (LBASCF) recommendation algorithm was proposed for the problem that traditional Collaborative Filtering (CF) recommendation algorithm does not consider the location information. Firstly, the cosine similarity and the Location-Based Asymmetric Similarity (LBAS) between users were calculated by the user-item rating matrix and the user's historical consumption location; secondly, a new user similarity measure was obtained by fusing the cosine similarity and location-based similarity. The blended similarity could reflect the user's preferences in both location and interest. Finally, based on the ratings of the user's nearest neighbors, new items were recommended to the user. The effectiveness of the algorithm was evaluated by using a dianping dataset and Foursquare dataset. The experimental results on the dianping dataset show that, compared with CF, the recall and precision of LBASCF were increased by 1.64% and 0.37% respectively; compared with the Location-Aware Recommender System (LARS), the recall and precision of LBASCF were increased by 1.53% and 0.35% respectively. The experimental results show that LBASCF can achieve better recommendation quality of the system based on the application of location-based services than CF and LARS.

Key words: Collaborative Filtering (CF), location-based service, personalized recommendation, location-aware, location-based user similarity

摘要： 为提升推荐系统的准确率,针对传统协同过滤(CF)推荐算法没有有效使用位置信息的问题,提出了一种基于位置的非对称相似性度量的协同过滤推荐算法(LBASCF)。首先,分别利用用户-商品评分矩阵和用户历史消费位置,计算出用户间的余弦相似性和基于位置的非对称相似性;其次,将余弦相似性与基于位置的相似性融合,得到一个新的非对称用户相似性,融合后的相似性能够同时反映用户在位置上和兴趣上的偏好;最后,根据用户的最近邻居对商品的评分向用户推荐新的商品。用某点评数据集和Foursquare数据集对算法的有效性进行了评估。在某点评数据集实验结果证明,与CF相比,LBASCF的召回率和精确率分别提高了1.64%和0.37%;与位置感知协同过滤推荐系统(LARS)方法比较,LBASCF的召回率和精确率分别提高了1.53%和0.35%。实验结果表明,LBASCF相对于CF和LARS在基于位置服务的应用中能够有效提高系统的推荐质量。

关键词: 协同过滤, 基于位置服务, 个性化推荐, 位置感知, 基于位置的用户相似性