Social Tag Embedding for the Recommendation with Sparse User-Item Interactions

Most of traditional recommender systems perform well only when sufficient user-item interactions, such as purchase records or ratings, have been obtained in advance, while suffering from poor performance in the scenario of sparse interactions. Addressing this problem, we propose a neural network based recommendation framework which is fed with user/item'soriginal tags as well as the expanded tags from social context. Through embedding the latent correlations between tags into distributed feature representations, our model uncovers the implicit relationships between users and items sufficiently, exhibiting superior performance no matter whether sufficient user-item interactions are available or not. Furthermore, our framework can be further tailored for link prediction in networks, since recommending an item to a user can be recognized as predicting a link between them. The extensive experiments on two real recommendation tasks, i.e., Weibo followship recommendation and Douban movie recommendation, justify our framework's superiority to the state-of-the-art methods.

[1]  Michael J. Muller,et al.  Make new friends, but keep the old: recommending people on social networking sites , 2009, CHI.

[2]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[3]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[4]  Yongzheng Zhang,et al.  Predicting purchase behaviors from social media , 2013, WWW.

[5]  Paul Covington,et al.  Deep Neural Networks for YouTube Recommendations , 2016, RecSys.

[6]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

[7]  John Riedl,et al.  Tagommenders: connecting users to items through tags , 2009, WWW '09.

[8]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[9]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[10]  Benjamin Schrauwen,et al.  Deep content-based music recommendation , 2013, NIPS.

[11]  Yoshua Bengio,et al.  Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.

[12]  Zhiyuan Liu,et al.  Topical Word Embeddings , 2015, AAAI.

[13]  Makbule Gulcin Ozsoy,et al.  From Word Embeddings to Item Recommendation , 2016, ArXiv.

[14]  Suleyman Cetintas,et al.  Recommending Tumblr Blogs to Follow with Inductive Matrix Completion , 2014, RecSys Posters.

[15]  John K. Debenham,et al.  Recommender System Based on Consumer Product Reviews , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[16]  Lei Yu,et al.  A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems , 2017, AAAI.

[17]  Steffen Rendle,et al.  Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.

[18]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[19]  J. Friedman.  Stochastic gradient boosting , 2002 .

[20]  Xiaodong He,et al.  A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems , 2015, WWW.

[21]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[22]  Junjun Zhang,et al.  An Integrated Tag Recommendation Algorithm Towards Weibo User Profiling , 2015, DASFAA.

[23]  Alejandro Bellogín,et al.  Content-based recommendation in social tagging systems , 2010, RecSys '10.

[24]  Thomas Hofmann,et al.  Collaborative filtering via gaussian probabilistic latent semantic analysis , 2003, SIGIR.

[25]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[26]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[27]  Pasquale Lops,et al.  Word Embedding Techniques for Content-based Recommender Systems: An Empirical Evaluation , 2015, RecSys Posters.

[28]  Dit-Yan Yeung,et al.  Collaborative Deep Learning for Recommender Systems , 2014, KDD.

[29]  Yanghua Xiao,et al.  Semantic-Based Recommendation Across Heterogeneous Domains , 2015, 2015 IEEE International Conference on Data Mining.

[30]  Li Liu,et al.  Fusing Social Networks with Deep Learning for Volunteerism Tendency Prediction , 2016, AAAI.

[31]  Oren Barkan,et al.  ITEM2VEC: Neural item embedding for collaborative filtering , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

[32]  Sanjeev Arora,et al.  Random Walks on Context Spaces: Towards an Explanation of the Mysteries of Semantic Word Embeddings , 2015, ArXiv.

[33]  Scott Sanner,et al.  AutoRec: Autoencoders Meet Collaborative Filtering , 2015, WWW.