File Download
Supplementary

postgraduate thesis: Social network based recommender systems

TitleSocial network based recommender systems
Authors
Issue Date2015
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Li, H. [李輝]. (2015). Social network based recommender systems. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5610991
AbstractRecommender systems have become de facto tools for suggesting items that are of potential interest to users and achieving great success in e-commerce. Many famous online vendors such as Amazon and Netix leverage recommender systems to advertise products to customers. Predicting a user's rating on an item is the fundamental recommendation task. Traditional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. Recently, approaches that use data from social networks to improve the accuracy of rating prediction are emerging. However, most of the social network based recommender systems only consider direct friendships and they are less effective when the targeted user has few social connections. In this thesis, we review important rating prediction approaches in traditional and social based recommender systems. We extend SNRS, a state-of-the-art social recommender system by considering classifying the correlations between pairs of users ratings to enhance accuracy and including more users in the temporal influence links of the target user to improve the coverage. In addition, we boosted the effectiveness of social recommender systems based on matrix factorization, by proposing two models that incorporate the overlapping community regularization into the matrix factorization framework differently. Our empirical studies on real data show that our approaches outperform baselines in both traditional and social network based recommender systems.
DegreeMaster of Philosophy
SubjectRecommender systems (Information filtering)
Online social networks
Dept/ProgramComputer Science
Persistent Identifierhttp://hdl.handle.net/10722/221187

 

DC FieldValueLanguage
dc.contributor.authorLi, Hui-
dc.contributor.author李輝-
dc.date.accessioned2015-11-04T23:11:56Z-
dc.date.available2015-11-04T23:11:56Z-
dc.date.issued2015-
dc.identifier.citationLi, H. [李輝]. (2015). Social network based recommender systems. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5610991-
dc.identifier.urihttp://hdl.handle.net/10722/221187-
dc.description.abstractRecommender systems have become de facto tools for suggesting items that are of potential interest to users and achieving great success in e-commerce. Many famous online vendors such as Amazon and Netix leverage recommender systems to advertise products to customers. Predicting a user's rating on an item is the fundamental recommendation task. Traditional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. Recently, approaches that use data from social networks to improve the accuracy of rating prediction are emerging. However, most of the social network based recommender systems only consider direct friendships and they are less effective when the targeted user has few social connections. In this thesis, we review important rating prediction approaches in traditional and social based recommender systems. We extend SNRS, a state-of-the-art social recommender system by considering classifying the correlations between pairs of users ratings to enhance accuracy and including more users in the temporal influence links of the target user to improve the coverage. In addition, we boosted the effectiveness of social recommender systems based on matrix factorization, by proposing two models that incorporate the overlapping community regularization into the matrix factorization framework differently. Our empirical studies on real data show that our approaches outperform baselines in both traditional and social network based recommender systems.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.lcshRecommender systems (Information filtering)-
dc.subject.lcshOnline social networks-
dc.titleSocial network based recommender systems-
dc.typePG_Thesis-
dc.identifier.hkulb5610991-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineComputer Science-
dc.description.naturepublished_or_final_version-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats