Content-Based Bipartite User-Image Correlation for Image Recommendation

The popularity of online social curation networks takes benefits from its convenience to retrieve, collect, sort and share multimedia contents among users. With increasing content and user intent gap, effective recommendation becomes highly desirable for its further development. In this paper, we propose a content-based bipartite graph for image recommendation in social curation networks. Bipartite graph employs given sparse user-image interactions to infer user-image correlation for recommendation. Beside given user-image interactions, the user interacted visual content also reveals valuable user preferences. Visual content is embedded into the bipartite graph to extend the correlation density and the recommendation scope simultaneously. Furthermore, the content similarity is employed for recommendation reranking to improve the visual quality of recommended images. Experimental results demonstrate that the proposed method enhances the recommendation ability of the bipartite graph effectively.

[1]  K. R. Venugopal,et al.  Image recommendation based on keyword relevance using absorbing Markov chain and image features , 2016, International Journal of Multimedia Information Retrieval.

[2]  Yi-Cheng Zhang,et al.  Heat conduction process on community networks as a recommendation model. , 2007, Physical review letters.

[3]  Julian J. McAuley,et al.  Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering , 2016, WWW.

[4]  Petter Holme,et al.  Network bipartivity. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Mohan S. Kankanhalli,et al.  ConTagNet: Exploiting User Context for Image Tag Recommendation , 2016, ACM Multimedia.

[6]  P. Bearman,et al.  Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks1 , 2004, American Journal of Sociology.

[7]  Michael A. Zarro,et al.  Pinterest: social collecting for #linking #using #sharing , 2012, JCDL '12.

[8]  Shang Mingsheng,et al.  Diffusion-Based Recommendation in Collaborative Tagging Systems , 2009 .

[9]  Michael A. Zarro,et al.  Social curation on the website Pinterest.com , 2012, ASIST.

[10]  Jun Li,et al.  Recommendation on social network based on graph model , 2012, Proceedings of the 31st Chinese Control Conference.

[11]  Jiebo Luo,et al.  Pinterest Board Recommendation for Twitter Users , 2015, ACM Multimedia.

[12]  Sourav S. Bhowmick,et al.  Social image tag recommendation by concept matching , 2011, ACM Multimedia.

[13]  Constantine Kotropoulos,et al.  Personalized and geo-referenced image recommendation using unified hypergraph learning and group sparsity optimization , 2014, 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[14]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[15]  Christoph Meinel,et al.  Extracting Image Context from Pinterest for Image Recommendation , 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

[16]  Gao Cong,et al.  A general graph-based model for recommendation in event-based social networks , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[17]  Tao Chen,et al.  Context-aware Image Tweet Modelling and Recommendation , 2016, ACM Multimedia.

[18]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Lei Zhang,et al.  Image Recommendation on Content-Based Bipartite Graph , 2017, ICIMCS.

[20]  Eric P. Xing,et al.  Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Weisi Lin,et al.  Personality Modeling Based Image Recommendation , 2015, MMM.

[22]  Marie-Francine Moens,et al.  Inferring User Interests on Social Media from Text and Images , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[23]  Jasmine Novak,et al.  Geographic routing in social networks , 2005, Proc. Natl. Acad. Sci. USA.

[24]  Yu-Chiang Frank Wang,et al.  Active learning based clothing image recommendation with implicit user preferences , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[25]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[26]  Wei Niu,et al.  Neural Personalized Ranking for Image Recommendation , 2018, WSDM.

[27]  M Ausloos,et al.  N-body decomposition of bipartite author networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  G. Ergun.  Human Sexual Contact Network as a Bipartite Graph , 2001, cond-mat/0111323.

[29]  Yang Yang,et al.  One of a Kind: User Profiling by Social Curation , 2014, ACM Multimedia.

[30]  Nicu Sebe,et al.  Faved! Biometrics: Tell Me Which Image You Like and I'll Tell You Who You Are , 2014, IEEE Transactions on Information Forensics and Security.

[31]  Daling Wang,et al.  An Approach for Clothing Recommendation Based on Multiple Image Attributes , 2016, WAIM.

[32]  S. Sitharama Iyengar,et al.  Image Recommendation Based on ANOVA Cosine Similarity , 2016 .