基于Location2vec的地点推荐算法
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摘要
在地点推荐应用中,传统的协同过滤推荐算法由于签到数据稀疏导致推荐效果不佳。为提高推荐效果并克服传统协同过滤推荐算法受到热门地点影响的不足,提出一种新的地点推荐算法。将签到地点转换为向量,通过向量的余弦相似性计算签到地点的地点相似性。标记签到频次较低的地点为冷门地点,以计算签到地点的用户相似性,结合地理因素的影响,生成对用户的推荐列表。实验结果表明,相比传统协同过滤推荐算法,该算法F1值提升了0.009以上,推荐效果更好。
In the location recommendation application,the traditional collaborative filtering recommendation algorithms are not effective due to the sparseness of the check-in data.In order to improve the recommendation effect and overcome the shortcomings of the traditional collaborative filtering recommendation algorithms affected by popular locations,this paper proposes a new location recommendation algorithm.The check-in location is transformed into a vector,the similarity between the locations is calculated by the cosine similarity of the vectors.The locations with low check-in frequency are marked as unpopular locations,which can be used to calculate the similarity of the users at the check-in location.The user's recommendation list is generated in conjunction with the influence of the geographical factors.Experimental results show that compared with the traditional collaborative filtering recommendation algorithms,the F1 value of the algorithm is improved by more than 0.009,and the recommended effect is better.
In the location recommendation application,the traditional collaborative filtering recommendation algorithms are not effective due to the sparseness of the check-in data.In order to improve the recommendation effect and overcome the shortcomings of the traditional collaborative filtering recommendation algorithms affected by popular locations,this paper proposes a new location recommendation algorithm.The check-in location is transformed into a vector,the similarity between the locations is calculated by the cosine similarity of the vectors.The locations with low check-in frequency are marked as unpopular locations,which can be used to calculate the similarity of the users at the check-in location.The user's recommendation list is generated in conjunction with the influence of the geographical factors.Experimental results show that compared with the traditional collaborative filtering recommendation algorithms,the F1 value of the algorithm is improved by more than 0.009,and the recommended effect is better.
引文
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[15] YANG Dingqi,ZHANG Daqing.Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs[J].IEEE Transactions on Systems Man and Cybernetics Systems,2014,45(1):129-142.
[2] 蒋翠清,疏得友,段锐.基于用户时空相似性的位置推荐算法[J].计算机工程,2018,44(7):177-182.
[3] YE Mao,YIN Peifeng.Location recommendation for location-based social networks[C]//Proceedings of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.New York,USA:ACM Press,2010:458-461.
[4] 任星怡,宋美娜,宋俊德.基于用户签到行为的兴趣点推荐[J].计算机学报,2017,40(1):28-51
[5] JANOWICZ K,LEE W C.What you are is when you are:the temporal dimension of feature types in location-based social networks[C]//Proceedings of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.New York,USA:ACM Press,2011:102-111.
[6] SERDYUKOV P,HANJALIC A.Personalized landmark recommendation based on geotags from photo sharing sites[C]//Proceedings of the 5th International AAAI Conference on Weblogs and Social Media.Barcelona,Spain:[s.n.],2011:622-625.
[7] WANG Wei,ZHANG Guangquan,LU Jie.Collaborative filtering with entropy-driven user similarity in recommender systems[J].International Journal of Intelligent Systems,2015,30(8):854-870.
[8] YE Mao,YIN Peifeng.Exploiting geographical influence for collaborative point-of-interest recommendation[C]//Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM Press,2011:325-334.
[9] CHENG Chen,YANG Haiqin.Fused matrix factorization with geographical and social influence in location-based social networks[C]//Proceedings of AAAI Conference on Artificial Intelligence.Toronto,Canada:[s.n.],2012:17-23.
[10] MIKOLOV T,LE Q V,SUTSKEVER I.Exploiting similarities among languages for machine translation[EB/OL].[2018-05-04].https://arxiv.org/pdf/1309.4168v1.pdf.
[11] MIKOLOV T,SUTSKEVER I.Distributed representations of words and phrases and their compositionality[C]//Proceedings of Advances in Neural Information Processing Systems.[S.l.]:Neural Information Processing Systems Foundation,Inc.,2013:3111-3119.
[12] KARYPIS G.Evaluation of item-based top-n recommendation algorithms[C]//Proceedings of the 10th International Conference on Information and Knowledge Management.New York,USA:ACM Press,2001:247-254.
[13] BREESE J S,HECKERMAN D,KADIE C.Empirical analysis of predictive algorithms for collaborative filtering[J].Uncertainty in Artificial Intelligence,2013,98(7):43-52.
[14] Stackoverflow.Calculate the center point of multiple latitude/longitude coordinate pairs[EB/OL].[2018-05-04].http://stackoverflow.com/questions/6671183/calculate-the-center-point-of-multiple-latitude-longitude-coordinate-pairs.
[15] YANG Dingqi,ZHANG Daqing.Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs[J].IEEE Transactions on Systems Man and Cybernetics Systems,2014,45(1):129-142.