TRRS: Temporal Recurrent Recommender System based on Time-sync Comments

Recent years has witnessed great emerge of online video websites, including the exploded number of videos and users. As a result, there appears a lot of personlized recommender systems. However there remain some challenging problems to tackle such as cold start problem, which scientists have made use of all kinds of sideinformation, e.g. gender, age or comments, to release. Currently a new type of video comments, called TSCs (TSC), plays a more and more important role in video watching activity. In this paper we utilize TSC to recommend videos for users. We developed a deep nueral network model called Temporal Recurrent Recommder System (TRRS) which combine multi-layers neural network to extract feature for users and videos. The first layer convert TSC to embeddings, then RNN layer analyze each comment from user or video, and fianlly the merge layer combine all output from prior layer and produce the feature. We use the feature from the network for users and videos to make personlized recommendation.

[1]  Guokun Lai,et al.  Explicit factor models for explainable recommendation based on phrase-level sentiment analysis , 2014, SIGIR.

[2]  Yi Zheng,et al.  Reading the Videos: Temporal Labeling for Crowdsourced Time-Sync Videos Based on Semantic Embedding , 2016, AAAI.

[3]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[4]  Alexander J. Smola,et al.  Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) , 2014, KDD.

[5]  Xu Chen,et al.  Learning to Rank Features for Recommendation over Multiple Categories , 2016, SIGIR.

[6]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[7]  Qiang Yang,et al.  Crowdsourced time-sync video tagging using temporal and personalized topic modeling , 2014, KDD.

[8]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[9]  Yiqun Liu,et al.  Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews , 2016, IJCAI.

[10]  Andrei Popescu-Belis,et al.  Multilingual Hierarchical Attention Networks for Document Classification , 2017, IJCNLP.

[11]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[12]  Donghyun Kim,et al.  Convolutional Matrix Factorization for Document Context-Aware Recommendation , 2016, RecSys.

[13]  Weijia Jia,et al.  Crowdsourced time-sync video tagging using semantic association graph , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[14]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[15]  Judith Masthoff,et al.  A Survey of Explanations in Recommender Systems , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[16]  Martin Ester,et al.  FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering , 2015, WSDM.

[17]  Quoc V. Le,et al.  Exploiting Similarities among Languages for Machine Translation , 2013, ArXiv.