Ruining He
ABSTRACT
Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard' recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as the need to model content in terms of its visual appearance, or even social dynamics, such as a preference toward a particular artist that is independent of the art they create. In this paper we build large-scale recommender systems to model the dynamics of a vibrant digital art community, Behance, consisting of tens of millions of interactions (clicks and 'appreciates') of users toward digital art. Methodologically, our main contributions are to model (a) rich content, especially in terms of its visual appearance; (b) temporal dynamics, in terms of how users prefer 'visually consistent' content within and across sessions; and (c) social dynamics, in terms of how users exhibit preferences both towards certain art styles, as well as the artists themselves.
Supplemental Material
- D. W. Aha, D. Kibler, and M. K. Albert. Instance-based learning algorithms. Machine learning, 1991. Google Scholar
Digital Library
- A. J. Chaney, D. M. Blei, and T. Eliassi-Rad. A probabilistic model for using social networks in personalized item recommendation. In RecSys, 2015. Google ScholarDigital Library
- S. Chen, J. L. Moore, D. Turnbull, and T. Joachims. Playlist prediction via metric embedding. In SIGKDD, 2012. Google ScholarDigital Library
- S. Chen, J. Xu, and T. Joachims. Multi-space probabilistic sequence modeling. In SIGKDD, 2013. Google ScholarDigital Library
- Y. Ding and X. Li. Time weight collaborative filtering. In CIKM, 2005. Google ScholarDigital Library
- S. Feng, X. Li, Y. Zeng, G. Cong, Y. M. Chee, and Q. Yuan. Personalized ranking metric embedding for next new poi recommendation. In IJCAI, 2015. Google ScholarDigital Library
- G. Guo, J. Zhang, and N. Yorke-Smith. Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In AAA, 2015. Google ScholarDigital Library
- R. He, C. Lin, J. Wang, and J. McAuley. Sherlock: sparse hierarchical embeddings for visually-aware one-class collaborative filtering. In IJCAI, 2016.Google ScholarDigital Library
- R. He and J. McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW, 2016. Google ScholarDigital Library
- R. He and J. McAuley. VBPR: visual bayesian personalized ranking from implicit feedback. In AAAI, 2016.Google ScholarDigital Library
- Y. Kalantidis, L. Kennedy, and L.-J. Li. Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos. In ICMR, 2013. Google ScholarDigital Library
- R. Klinkenberg. Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis, 2004. Google ScholarDigital Library
- Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In SIGKDD, 2008. Google ScholarDigital Library
- Y. Koren. Collaborative filtering with temporal dynamics. Communications of the ACM, 2010. Google ScholarDigital Library
- Y. Koren and R. Bell. Advances in collaborative filtering. In Recommender Systems Handbook. Springer, 2011.Google ScholarCross Ref
- H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In SIGIR, 2009. Google ScholarDigital Library
- H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In CIKM, 2008. Google ScholarDigital Library
- B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Using sequential and non-sequential patterns in predictive web usage mining tasks. In ICDM, 2002. Google ScholarDigital Library
- F. Niu, B. Recht, C. Ré, and S. J. Wright. Hogwild: A lock-free approach to parallelizing stochastic gradient descent. In NIPS, 2011. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: bayesian personalized ranking from implicit feedback. In UAI, 2009. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In WWW, 2010. Google ScholarDigital Library
- G. Shani, R. I. Brafman, and D. Heckerman. An mdp-based recommender system. In UAI, 2002. Google ScholarDigital Library
- K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. 2015.Google Scholar
- L. van der Maaten. Accelerating t-SNE using tree-based algorithms. JMLR, 2014. Google ScholarDigital Library
- H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In SIGKDD, 2003. Google ScholarDigital Library
- K. Yamaguchi, M. Kiapour, and T. Berg. Paper doll parsing: Retrieving similar styles to parse clothing items. In ICCV, 2013. Google ScholarDigital Library
- W. Yang, P. Luo, and L. Lin. Clothing co-parsing by joint image segmentation and labeling. In CVPR, 2014. Google ScholarDigital Library
- T. Zhao, J. McAuley, and I. King. Leveraging social connections to improve personalized ranking for collaborative filtering. In CIKM, 2014. Google ScholarDigital Library
- A. Zimdars, D. M. Chickering, and C. Meek. Using temporal data for making recommendations. In UAI, 2001. Google ScholarDigital Library
Index Terms
- Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Recommendations
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
A framework for diversifying recommendation lists by user interest expansion
Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
Merging visual features and temporal dynamics in sequential recommendation
AbstractWith the development of social networking and mobile computing technologies, data analysis in the fashion field has increasingly focused on visual features. The main features currently used in the recommendation methods include non-...
Comments