Improved one-class collaborative filtering for online recommendation

Recommended systems are becoming important and popular for online shopping platforms and vendors. Moreover, one-class collaborative filtering, which is based on the modeling of the feedback records of E-commercial website consumers, is one of the most widely used recommendation algorithms both academically and practically. However, one significant drawback of the existing techniques is that they typically do nor consider ratings and visual-temporal contexts, which are useful and important in modeling user behaviors. Therefore, to address this problem, we propose a new recommendation algorithm which is based on the combination of image features, user feedback ratings, and product evolution trends. The image feature can be extracted automatically using deep convolution neural network. Thus, our technique, which is essentially a time-aware visual mode, can represent the different visual feature preference of users over time. Our model is evaluated using the widely adopted Amazon online data and shown significantly improvements.

[1]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[2]  Wei Chu,et al.  Personalized recommendation on dynamic content using predictive bilinear models , 2009, WWW '09.

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

[4]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[5]  Yi-Cheng Zhang,et al.  Solving the apparent diversity-accuracy dilemma of recommender systems , 2008, Proceedings of the National Academy of Sciences.

[6]  Yannis Kalantidis,et al.  Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos , 2013, ICMR.

[7]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[8]  Julian J. McAuley,et al.  VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback , 2015, AAAI.

[9]  Henry Lieberman,et al.  Letizia: An Agent That Assists Web Browsing , 1995, IJCAI.

[10]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[11]  Ivan Koychev,et al.  Learning to recommend from positive evidence , 2000, IUI '00.

[12]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[13]  Javed Mostafa,et al.  A multilevel approach to intelligent information filtering: model, system, and evaluation , 1997, TOIS.

[14]  Xue Li,et al.  Time weight collaborative filtering , 2005, CIKM '05.