融合谱聚类和多因素影响的兴趣点推荐方法

Point-of-Interest Recommendation with Spectral Clustering and Multi-Factors

【目的】提高基于位置的社交网络的推荐算法的运行效率并降低稀疏数据对推荐效果的影响,提高兴趣点推荐准确率指标等。【方法】使用自适应谱聚类方法对用户进行分组,将组内用户访问过的兴趣点组成待推荐集合,综合考虑4个方面的影响,计算待推荐集合中兴趣点的吸引力评分,向用户推荐评分较高的兴趣点。【结果】在两种真实的基于位置的社交网络数据集Gowalla、Foursquare中进行实验。实验结果表明,推荐兴趣点个数为2时,推荐准确率分别为11.4%、7.4%,与对比方法 Lore相比准确率分别提高3.2%、1.1%;运行时间为50 644.5 s、406 224.7 s,分别缩短16 961.5 s、227 248.6 s。【局限】聚类效果的好坏对兴趣点的筛选结果有较大影响,因此所提算法对用户聚类分组效果有一定依赖性。【结论】该算法易于执行,执行效率较高,并且可以融合各种方法充分利用LBSN这种异质网络中的丰富语义信息来提升准确率。

[Objective] This paper tries to improve the recommendation algorithm for Location-Based Social Networks(LBSN) and reduce the impacts of sparse data on recommendation precision. [Methods] First, we used the adaptive spectral clustering technique to group the users. Then, we created the recommending candidates for the point of interests(POIs) visited by the users. Finally, we calculated the attracting scores of the candidate sets and generated the recommended POIs with higher scores. [Results] We examined the new model with two real LBSN data sets: Gowalla and Foursquare, and set the recommended number of POIs as 2. Our model’s precision reached 11.4% and 7.4%, which were 3.2% and 1.1% higher than the Lore model. The new model’s running time reduced to 50 644.53 s and 406 224.7 s(16 961.49 s and 227 248.6 s shorter than the benchmark model).[Limitations] The clustering algorithm could influence the screening of POIs. [Conclusions] The proposed model could effectively improve the recommendation precision of heterogeneous networks(i.e.,LBSN).