GeoSRS: A hybrid social recommender system for geolocated data

Abstract We present GeoSRS, a hybrid recommender system for a popular location-based social network (LBSN), in which users are able to write short reviews on the places of interest they visit. Using state-of-the-art text mining techniques, our system recommends locations to users using as source the whole set of text reviews in addition to their geographical location. To evaluate our system, we have collected our own data sets by crawling the social network Foursquare. To do this efficiently, we propose the use of a parallel version of the Quadtree technique, which may be applicable to crawling/exploring other spatially distributed sources. Finally, we study the performance of GeoSRS on our collected data set and conclude that by combining sentiment analysis and text modeling, GeoSRS generates more accurate recommendations. The performance of the system improves as more reviews are available, which further motivates the use of large-scale crawling techniques such as the Quadtree.

[1]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

[2]  Daniel Jurafsky,et al.  Generating Recommendation Dialogs by Extracting Information from User Reviews , 2013, ACL.

[3]  Bernd Resch,et al.  Towards the Live City – Paving the Way to Real-time Urbanism , 2012 .

[4]  Mao Ye,et al.  Location recommendation for location-based social networks , 2010, GIS '10.

[5]  Panagiotis Symeonidis,et al.  Location-Based Social Networks , 2014 .

[6]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[7]  Adam Tauman Kalai,et al.  Trust-based recommendation systems: an axiomatic approach , 2008, WWW.

[8]  Sang-Bum Kim,et al.  Effective Methods for Improving Naive Bayes Text Classifiers , 2002, PRICAI.

[9]  D. Böhning.  Multinomial logistic regression algorithm , 1992 .

[10]  Mamadou Diaby,et al.  Toward the next generation of recruitment tools: An online social network-based job recommender system , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[11]  AdomaviciusGediminas,et al.  Toward the Next Generation of Recommender Systems , 2005 .

[12]  Ahmed Eldawy,et al.  Personalization, Socialization, and Recommendations in Location-based Services 2.0 , 2011, VLDB 2011.

[13]  Xiaohui Yu,et al.  Modeling and Predicting the Helpfulness of Online Reviews , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[14]  Karen Spärck Jones.  A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.

[15]  Georg Groh,et al.  Recommendations in taste related domains: collaborative filtering vs. social filtering , 2007, GROUP.

[16]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[17]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[18]  Yi-Cheng Zhang,et al.  Recommender Systems , 2012, ArXiv.

[19]  David M. Blei,et al.  Supervised Topic Models , 2007, NIPS.

[20]  Yue Lu.  Exploiting Social Context for Review Quality Prediction , 2010 .

[21]  Bruno Ribeiro,et al.  Online dating recommendations: matching markets and learning preferences , 2014, WWW.

[22]  Kanna Al Falahi,et al.  Social Networks and Recommender Systems: A World of Current and Future Synergies , 2012 .

[23]  Boi Faltings,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Recommendation Using Textual Opinions , 2022 .

[24]  Félix Hernández-del-Olmo,et al.  Evaluation of recommender systems: A new approach , 2008, Expert Syst. Appl..

[25]  Thorsten Strufe,et al.  A recommendation system for spots in location-based online social networks , 2011, SNS '11.

[26]  Mouzhi Ge,et al.  Beyond accuracy: evaluating recommender systems by coverage and serendipity , 2010, RecSys '10.

[27]  Wei-Ying Ma,et al.  Recommending friends and locations based on individual location history , 2011, ACM Trans. Web.

[28]  GaudiosoElena,et al.  Evaluation of recommender systems , 2008 .

[29]  Kun Yang,et al.  Social Recommendation with Interpersonal Influence , 2010, ECAI.

[30]  Zhu Zhang,et al.  Utility scoring of product reviews , 2006, CIKM '06.

[31]  Stathes Hadjiefthymiades,et al.  Facing the cold start problem in recommender systems , 2014, Expert Syst. Appl..

[32]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[33]  Guy Shani,et al.  Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.

[34]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[35]  Soo-Min Kim,et al.  Automatically Assessing Review Helpfulness , 2006, EMNLP.

[36]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[37]  Panagiotis Symeonidis,et al.  Recommender Systems for Location-based Social Networks , 2014, Springer Briefs in Electrical and Computer Engineering.

[38]  Ido Guy,et al.  Social Recommender Systems , 2015, Recommender Systems Handbook.

[39]  Xin Jin,et al.  Semantically Enhanced Collaborative Filtering on the Web , 2003, EWMF.

[40]  Shivakant Mishra,et al.  Enhancing group recommendation by incorporating social relationship interactions , 2010, GROUP.

[41]  Regina Barzilay,et al.  Multiple Aspect Ranking Using the Good Grief Algorithm , 2007, NAACL.

[42]  Anthony Jameson,et al.  More than the sum of its members: challenges for group recommender systems , 2004, AVI.

[43]  Iryna Gurevych,et al.  Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations , 2009, TSA@CIKM.

[44]  BurkeRobin.  Hybrid Recommender Systems , 2002 .

[45]  Dunja Mladenic,et al.  Web Mining: From Web to Semantic Web , 2004, Lecture Notes in Computer Science.

[46]  Feng Xia,et al.  Folksonomy based socially-aware recommendation of scholarly papers for conference participants , 2014, WWW.

[47]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[48]  Dongre Deepak Mahapatrav,et al.  LARS*: An Efficient and Scalable Location-Aware Recommender System , 2017 .

[49]  Jafar Habibi,et al.  FeedbackTrust: using feedback effects in trust-based recommendation systems , 2009, RecSys '09.

[50]  Yu Zheng,et al.  Tutorial on Location-Based Social Networks , 2012 .

[51]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[52]  Hanan Samet,et al.  The Quadtree and Related Hierarchical Data Structures , 1984, CSUR.

[53]  Peter Van Roy,et al.  Trust-Based Recommendation: An Empirical Analysis , 2012, SNA-KDD 2012.

[54]  Finn Årup Nielsen,et al.  A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.

[55]  Karl Aberer,et al.  SoCo: a social network aided context-aware recommender system , 2013, WWW.

[56]  Martin Ester,et al.  TrustWalker: a random walk model for combining trust-based and item-based recommendation , 2009, KDD.

[57]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[58]  Zhu Wang,et al.  A sentiment-enhanced personalized location recommendation system , 2013, HT.

[59]  John K. Debenham,et al.  Informed Recommender: Basing Recommendations on Consumer Product Reviews , 2007, IEEE Intelligent Systems.