Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conduct...Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-interests(POIs) on the actual output of recommender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model(GSBPR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.展开更多
位置社交网络(Location Based Social Network,LBSN)的发展,为兴趣点推荐提供丰富的数据资源。基于地理影响的推荐算法是兴趣点推荐的热门研究话题,而现有的推荐算法缺乏对用户个性化行为的分析。因此,提出一种基于用户空间相似性的兴...位置社交网络(Location Based Social Network,LBSN)的发展,为兴趣点推荐提供丰富的数据资源。基于地理影响的推荐算法是兴趣点推荐的热门研究话题,而现有的推荐算法缺乏对用户个性化行为的分析。因此,提出一种基于用户空间相似性的兴趣点推荐算法。首先,利用用户签到数据构建空间分布相似性模型;其次,引入削减因子,提高具有相同签到记录的用户权重;最后,线性融合用户及空间分布性相似性模型对Top-N兴趣点进行推荐,并进行实验验证。实验结果表明,该算法有效提高了兴趣点推荐的质量。展开更多
基金supported by National Basic Research Program of China (2012CB719905)National Natural Science Funds of China (41201404)Fundamental Research Funds for the Central Universities of China (2042018gf0008)
文摘Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-interests(POIs) on the actual output of recommender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model(GSBPR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.
文摘位置社交网络(Location Based Social Network,LBSN)的发展,为兴趣点推荐提供丰富的数据资源。基于地理影响的推荐算法是兴趣点推荐的热门研究话题,而现有的推荐算法缺乏对用户个性化行为的分析。因此,提出一种基于用户空间相似性的兴趣点推荐算法。首先,利用用户签到数据构建空间分布相似性模型;其次,引入削减因子,提高具有相同签到记录的用户权重;最后,线性融合用户及空间分布性相似性模型对Top-N兴趣点进行推荐,并进行实验验证。实验结果表明,该算法有效提高了兴趣点推荐的质量。
