User Modeling And Content Recommendation In Location-based Social Media

Location-based social medias began to emerge with the rapid development of mobile Internet technology and widespread popularity of smart mobile devices,and have produced a large amount of location-aware user behavior data.In the era of big data,personalized recommendation,which models user behavior data,is the core technology for helping users to overcome the information overload problem and promoting the development of enterprise precision marketing.Traditional recommendation methods do not fully consider location information and its interactions with a variety of heterogeneous data,thus they can not obtain good recommendation results in this type of social media.This thesis aims at conducting systematic research on four typical user modeling and recommendation problems in location-based social medias based on the theories of data mining and machine learning to promote the further study of location-based social media in research field and deployment of recommendation applications in industry field.The research questions and technical contributions of this thesis are summarized as follows,1.Event-based group recommendation: to solve the problem that traditional group recommendation methods are proposed only for online virtual groups,this thesis fully explores the influence of location factors on users' participation in groups and proposes a unified model,i.e.,PTARMIGAN,which combines the designed location-based features standard recommendation methods.The experimental results show that the proposed model is better than several other related methods in group recommendation,and further verify that considering location information is beneficial for predicting users to join event-based groups.2.Cold-start local event recommendation: to overcome the shortages that previous studies for event recommendation neglecting to address the cold-start events,this thesis proposes a collective Bayesian Poisson factorization model,i.e.,CBPF.This model fully considers heterogeneous information such as organizers of events,introduction of events,and venues of events,and designs an effective learning algorithm to obtain representations of cold-start events.The experimental results demonstrate CBPF outperforms several previous methods,and find organizers of events have the largest impact on whether users to join events.3.Time-aware next location recommendation: to exploit the joint influence of tem-poral factors,social relations,and current locations for user's preference to next locations,this thesis proposes LTSCR by incorporating temporal information and social relation into the basic collaborative retrieval model.It regards a target user,the location where he currently stays,and temporal information as an implicit query,and ranking locations according to the relevance scores between the user and locations.The experimental results show that incorporating spatial-temporal information and social relations into collaborative retrieval model can obtain better performance on this task.4.User-item connected review modeling and rating prediction: To capture the characteristic that topics of a review are associated with its belonging user and location in location and review-based social medias,this thesis develops prior-based dual additive latent Dirichlet allocation(PDA-LDA)model.It associates parameters of Dirichlet distribution with user and item topic factors to influence topic distribution of reviews.The experimental results show PDA-LDA behaves well for text modeling and its generated topic features can benefit rating prediction.