基于用户兴趣和地理因素的兴趣点推荐方法
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摘要
在基于位置的社交网络中,协同过滤作为目前应用最广泛的推荐技术,存在数据稀疏性和冷启动等问题。针对协同过滤算法的不足,提出了一种结合用户兴趣和地理因素的兴趣点推荐算法。该方法首先通过自适应带宽的核密度分布、朴素贝叶斯算法以及兴趣点的流行度挖掘用户的地理偏好,并根据地理偏好模型筛选出一部分候选推荐兴趣点;然后,为了克服协同过滤算法的数据稀疏性问题和用户冷启动问题,结合用户签到相似性、类别信息和用户信任度构建用户偏好模型进行兴趣点推荐;最后,使用Yelp数据集进行实验分析,结果表明所提出的基于用户兴趣和地理因素的兴趣点推荐模型取得了良好的推荐效果。
In the location-based social network,collaborative filtering is the most widely used recommended technology,but it has some drawbacks,such as data sparsity and cold start.In light of this,this paper presented a point of interest(POI) recommendation algorithm combining user's interest and geographic factors.In this method,firstly,the adaptive kernel density distribution,naive Bayesian algorithm and the popularity of POIs are combined to mine user's geographi-cal preferences,and some candidate recommended POIs are screened out according to the geographical preference model.Then,in order to overcome the problems of data sparsity and cold start in collaborative filtering algorithm,a user pre-ference model is constructed to carry out POI recommendation based on the similarities of user checked-in,category information and user trust.Finally,the Yelp data set was used to conduct the experimental analysis.The results show that the proposed POI recommendation model based on user's interest and geographical factor obtains good recommendation effect.
In the location-based social network,collaborative filtering is the most widely used recommended technology,but it has some drawbacks,such as data sparsity and cold start.In light of this,this paper presented a point of interest(POI) recommendation algorithm combining user's interest and geographic factors.In this method,firstly,the adaptive kernel density distribution,naive Bayesian algorithm and the popularity of POIs are combined to mine user's geographi-cal preferences,and some candidate recommended POIs are screened out according to the geographical preference model.Then,in order to overcome the problems of data sparsity and cold start in collaborative filtering algorithm,a user pre-ference model is constructed to carry out POI recommendation based on the similarities of user checked-in,category information and user trust.Finally,the Yelp data set was used to conduct the experimental analysis.The results show that the proposed POI recommendation model based on user's interest and geographical factor obtains good recommendation effect.
引文
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[12] LIN K,WANG J,ZHANG Z,et al.Adaptive location recommendation algorithm based on location-based social networks[C]//International Conference on Computer Science & Education.Cambridge:IEEE Press,2015:137-142.
[13] WANG R Q,PAN J,LI Y X,Research on collaborative recommendation method based on multiple data sources of social network.Telecommunications Science[J].Telecommunications Science,2015,31(6):78-84.(in Chinese)王瑞琴,潘俊,李一啸.基于多社交数据源的协同推荐方法研究[J].电信科学,2015,31(6):78-84.
[14] HE M,XIAO R,LIU W S,et al.Collaborative Filtering Recommendation Algorithm Combing Category Information and User Interests [J].Telecommunications Science,2017,44(8):230-235.(in Chinese)何明,肖润,刘伟世,等.融合类别信息和用户兴趣度的协同过滤推荐算法[J].计算机科学,2017,44(8):230-235.
[15] Yelp.Challenge Data Set [OL].(2015-12-24).http://www.yelp.com/dataset_change.
[16] ZHANG J D,CHOW C Y.GeoSoCa:Exploiting Geographical,Social and Categorical Correlations for Point-of-Interest Recommendations[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval.Santiago:ACM Press,2015:443-452.
[2] YU Y,WANG H,SUN S,et al.Exploiting location significance and user authority for point-of-interest recommendation[C]//Advances in Knowledge Discovery and Data Mining- 21st Paci-fic-Asia Conference.South Korea:PAKDD Press,2017:119-130.
[3] YANG S,HUANG G,XIANG Y,et al.Modeling User Preferences on Spatiotemporal Topics for Point-of-Interest Recommendation[C]//IEEE International Conference on Services Computing.Honolulu:IEEE Press,2017:204-211.
[4] LIM K H,CHAN J,LECKIE C,et al.Personalized trip recommendation for tourists based on user interests,points of interest visit durations and visit recency[J].Knowledge & Information Systems,2017(2):1-32.
[5] XIE M,WANG S,WANG H,et al.Learning Graph-based POI Embedding for Location based Recommendation[C]//ACM International on Conference on Information and Knowledge Mana-gement.Indianapolis:ACM,2016:15-24.
[6] FANG M Y,DAI B R.Power of Bosom Friends,POI Recom- mendation by Learning Preference of Close Friends and Similar Users[C]//Big Data Analytics and Knowledge Discovery.Porto:Springer Press,2016:179-192.
[7] ZHANG J D,CHOW C Y,ZHENG Y.ORec:An Opinion-Based Point-of-Interest Recommendation Framework[C]//ACM International on Conference on Information and Knowledge Mana-gement.Melbourne:ACM,2015:1641-1650.
[8] YE M,YIN P,LEE W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C]//Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.Beijing:ACM Press,2011:325-334.
[9] ZHANG J D,CHOW C Y,LI Y.iGeoRec:A Personalized and Efficient Geographical Location Recommendation Framework[J].IEEE Transactions on Services Computing,2015,8(5):701-714.
[10] LI H,HONG R,ZHU S,et al.Point-of-Interest Recommender Systems:A Separate-Space Perspective[C]//IEEE International Conference on Data Mining.Barcelona:IEEE Press,2016:231-240.
[11] CHENG C,HUANG J,ZHONG N.Point-of-Interest Recommendations by Unifying Multiple Correlations[C]//Web-Age Information Management.Nanchang:Springer Press,2016:178-190.
[12] LIN K,WANG J,ZHANG Z,et al.Adaptive location recommendation algorithm based on location-based social networks[C]//International Conference on Computer Science & Education.Cambridge:IEEE Press,2015:137-142.
[13] WANG R Q,PAN J,LI Y X,Research on collaborative recommendation method based on multiple data sources of social network.Telecommunications Science[J].Telecommunications Science,2015,31(6):78-84.(in Chinese)王瑞琴,潘俊,李一啸.基于多社交数据源的协同推荐方法研究[J].电信科学,2015,31(6):78-84.
[14] HE M,XIAO R,LIU W S,et al.Collaborative Filtering Recommendation Algorithm Combing Category Information and User Interests [J].Telecommunications Science,2017,44(8):230-235.(in Chinese)何明,肖润,刘伟世,等.融合类别信息和用户兴趣度的协同过滤推荐算法[J].计算机科学,2017,44(8):230-235.
[15] Yelp.Challenge Data Set [OL].(2015-12-24).http://www.yelp.com/dataset_change.
[16] ZHANG J D,CHOW C Y.GeoSoCa:Exploiting Geographical,Social and Categorical Correlations for Point-of-Interest Recommendations[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval.Santiago:ACM Press,2015:443-452.