基于上下文感知和个性化度量嵌入的下一个兴趣点推荐
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摘要
随着基于位置的社交网络推荐系统的逐步发展,兴趣点推荐成为了研究热门。兴趣点推荐的研究旨在为用户推荐兴趣点,并且为商家提供广告投放和潜在客户发掘等服务。由于用户签到行为的数据具有高稀疏性,为兴趣点推荐带来很大的挑战。许多研究工作结合地理影响、时间效应、社会相关性等方面的因素来提高兴趣点推荐的性能。然而,在大多数兴趣点推荐的工作中,用户访问的周期性习惯和伴随用户偏好的上下文情境信息没有被深度地挖掘。而且,下一个兴趣点推荐中一直存在着数据的高稀疏度。基于以上考虑,针对用户签到的数据稀疏性问题,将用户周期性行为模式归结为上下文情境信息,提出了一种基于上下文感知的个性化度量嵌入推荐算法,同时将用户签到的上下文情境信息考虑进来,从而丰富有效数据,缓解数据稀疏性问题,提高推荐的准确率,并且进一步优化算法,降低时间复杂度。在两个真实数据集上的实验分析表明,本文提出的算法具有更好的推荐效果。
With the rapid development of Location-Based Social Networks(LBSN)recommender system,Point-of-Interest(POI)recommendation has become a hot topic.The research of POI recommendation aims to recommend POIs for users and to provide services such as advertising and potential customer discovery.Due to the high data sparseness of users' check-ins,POI recommendation faces a great challenge.Many researches combine geographical influence,time awareness,social relevance and other factors to improve the performance of POI recommendation.However,in most POI recommendation researches,the periodicity of mobility and the user preference varying with the change of contextual scenario have not been excavated in depth.Moreover,there exists high data sparseness in Next POI recommendation.Based on the above considerations,this paper proposes a Context-aware Personalized Metric Embedding(CPME)algorithm,which is based on the user's periodic behavior pattern.It takes into account the contextual information of users' check-ins,which can enrich the valid data,alleviate the data sparseness,improve the recommendation accuracy,and further optimize the algorithm to reduce the time complexity.The experimental analysis on two real LBSN datasets show that the proposed algorithm has better recommendation performance.
With the rapid development of Location-Based Social Networks(LBSN)recommender system,Point-of-Interest(POI)recommendation has become a hot topic.The research of POI recommendation aims to recommend POIs for users and to provide services such as advertising and potential customer discovery.Due to the high data sparseness of users' check-ins,POI recommendation faces a great challenge.Many researches combine geographical influence,time awareness,social relevance and other factors to improve the performance of POI recommendation.However,in most POI recommendation researches,the periodicity of mobility and the user preference varying with the change of contextual scenario have not been excavated in depth.Moreover,there exists high data sparseness in Next POI recommendation.Based on the above considerations,this paper proposes a Context-aware Personalized Metric Embedding(CPME)algorithm,which is based on the user's periodic behavior pattern.It takes into account the contextual information of users' check-ins,which can enrich the valid data,alleviate the data sparseness,improve the recommendation accuracy,and further optimize the algorithm to reduce the time complexity.The experimental analysis on two real LBSN datasets show that the proposed algorithm has better recommendation performance.
引文
[1]Chen S,Moore J L,Turnbull D,et al.Playlist prediction via metric embedding[C]∥Proc of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Figure 9 F1-score of the two city datasets varies with N图9两个城市数据集的F1-score随N变化的结果Mining,2012:714-722.
[2]Cho E,Myers S A,Leskovec J.Friendship and mobility:User movement in location-based social networks[C]∥Proc of the17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2011:1082-1090.
[3]Li X,Cong G,Li X L,et al.Rank-geofm:A ranking based geographical factorization method for point of interest recommendation[C]∥Proc of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval,2015:433-442.
[4]Lian D,Zhao C,Xie X,et al.GeoMF:Joint geographical modeling and matrix factorization for point-of-interest recommendation[C]∥Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2014:831-840.
[5]Ye M,Yin P,Lee W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C]∥Proc of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval,2011:325-334.
[6]Yuan Q,Cong G,Ma Z,et al.Time-aware point-of-interest recommendation[C]∥Proc of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval,2013:363-372.
[7]Cheng C,Yang H,Lyu M R,et al.Where you like to go next:Successive point-of-interest recommendation[C]∥Proc of the22th International Joint Conference on Artificial Intelligence,2013:2605-2611.
[8]Ye J,Zhu Z,Cheng H.What's your next move:User activity prediction in location-based social networks[C]∥Proc of the2013SIAM International Conference on Data Mining,2013:171-179.
[9]Rendle S,Freudenthaler C,Schmidt-Thieme L.Factorizing personalized Markov chains for next-basket recommendation[C]∥Proc of the 19th International Conference on World Wide Web,2010:811-820.
[10]Feng S,Li X,Zeng Y,et al.Personalized ranking metric embedding for next new POI recommendation[C]∥Proc of the24th International Joint Conference on Artificial Intelligence,2015:2069-2075.
[11]Zheng Y,Zhang L,Xie X,et al.Mining interesting locations and travel sequences from GPS trajectories[C]∥Proc of the18th International Conference on World Wide Web,2009:791-800.
[12]Liu B,Fu Y,Yao Z,et al.Learning geographical preferences for point-of-interest recommendation[C]∥Proc of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2013:1043-1051.
[13]Yang D,Zhang D,Zheng V W,et al.Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2015,45(1):129-142.
[14]Tang J,Aggarwal C,Liu H.Recommendations in signed social networks[C]∥Proc of the 25th International Conference on World Wide Web,2016:31-40.
[15]Lian D,Ge Y,Zhang F,et al.Content-aware collaborative filtering for location recommendation based on human mobility data[C]∥Proc of the 15th IEEE International Conference on Data Mining,2015:261-270.
[16]He J,Li X,Liao L,et al.Inferring apersonalized next pointof-interest recommendation model with latent behavior patterns[C]∥Proc of the 30th AAAI Conference on Artificial Intelligence,2016:137-143.
[17]Xie M,Yin H,Xu F,et al.Graph-based metric embedding for next POI recommendation[C]∥Proc of the 17th International Conference on Web Information Systems Engineering,2016:207-222.
[18]Gormley M R,Yu M,Dredze M.Improved relation extraction with feature-rich compositional embedding models[C]∥Proc of 2015Conference on Empirical Methods on Natural Language Processing,2015:1-12.
[19]Rendle S,Freudenthaler C,Gantner Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]∥Proc of the25th Conference on Uncertainty in Artificial Intelligence,2009:452-461.
[2]Cho E,Myers S A,Leskovec J.Friendship and mobility:User movement in location-based social networks[C]∥Proc of the17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2011:1082-1090.
[3]Li X,Cong G,Li X L,et al.Rank-geofm:A ranking based geographical factorization method for point of interest recommendation[C]∥Proc of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval,2015:433-442.
[4]Lian D,Zhao C,Xie X,et al.GeoMF:Joint geographical modeling and matrix factorization for point-of-interest recommendation[C]∥Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2014:831-840.
[5]Ye M,Yin P,Lee W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C]∥Proc of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval,2011:325-334.
[6]Yuan Q,Cong G,Ma Z,et al.Time-aware point-of-interest recommendation[C]∥Proc of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval,2013:363-372.
[7]Cheng C,Yang H,Lyu M R,et al.Where you like to go next:Successive point-of-interest recommendation[C]∥Proc of the22th International Joint Conference on Artificial Intelligence,2013:2605-2611.
[8]Ye J,Zhu Z,Cheng H.What's your next move:User activity prediction in location-based social networks[C]∥Proc of the2013SIAM International Conference on Data Mining,2013:171-179.
[9]Rendle S,Freudenthaler C,Schmidt-Thieme L.Factorizing personalized Markov chains for next-basket recommendation[C]∥Proc of the 19th International Conference on World Wide Web,2010:811-820.
[10]Feng S,Li X,Zeng Y,et al.Personalized ranking metric embedding for next new POI recommendation[C]∥Proc of the24th International Joint Conference on Artificial Intelligence,2015:2069-2075.
[11]Zheng Y,Zhang L,Xie X,et al.Mining interesting locations and travel sequences from GPS trajectories[C]∥Proc of the18th International Conference on World Wide Web,2009:791-800.
[12]Liu B,Fu Y,Yao Z,et al.Learning geographical preferences for point-of-interest recommendation[C]∥Proc of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2013:1043-1051.
[13]Yang D,Zhang D,Zheng V W,et al.Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2015,45(1):129-142.
[14]Tang J,Aggarwal C,Liu H.Recommendations in signed social networks[C]∥Proc of the 25th International Conference on World Wide Web,2016:31-40.
[15]Lian D,Ge Y,Zhang F,et al.Content-aware collaborative filtering for location recommendation based on human mobility data[C]∥Proc of the 15th IEEE International Conference on Data Mining,2015:261-270.
[16]He J,Li X,Liao L,et al.Inferring apersonalized next pointof-interest recommendation model with latent behavior patterns[C]∥Proc of the 30th AAAI Conference on Artificial Intelligence,2016:137-143.
[17]Xie M,Yin H,Xu F,et al.Graph-based metric embedding for next POI recommendation[C]∥Proc of the 17th International Conference on Web Information Systems Engineering,2016:207-222.
[18]Gormley M R,Yu M,Dredze M.Improved relation extraction with feature-rich compositional embedding models[C]∥Proc of 2015Conference on Empirical Methods on Natural Language Processing,2015:1-12.
[19]Rendle S,Freudenthaler C,Gantner Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]∥Proc of the25th Conference on Uncertainty in Artificial Intelligence,2009:452-461.