CMBPR: Category-Aided Multi-Channel Bayesian Personalized Ranking for Short Video Recommendation

Due to the viewing convenience for social media users’ fragmented time, the short video has become a new carrier for users’ network demands on information spread, news reading, social contact, entertainment, and leisure. Therefore, short video recommendation is one of the most important research topics in social media. Current short video recommendation algorithms mainly focus on detecting user’s social attributes, developing cross-domain information and so on, few researchers combine video category information and multi-behavior information together. This paper proposes a content-based recommendation algorithm Category-aided Multi-channel Bayesian Personalized Ranking (CMBPR) for short video recommendation, which integrates users’ rich preference information by considering the difference among both different video categories and different user interactions. The experimental results demonstrate the effectiveness of the CMBPR video recommendation algorithm, which achieves a significantly higher recommendation accuracy than the traditional video recommendation algorithms and solves the influence of the “Long Tail” effect.

[1]  Martha Larson,et al.  Bayesian Personalized Ranking with Multi-Channel User Feedback , 2016, RecSys.

[2]  Hendry,et al.  Video Recommendation System Based on Personalized Ontology and Social Network , 2015 .

[3]  Lifeng Sun,et al.  Social-Aware Video Recommendation for Online Social Groups , 2017, IEEE Transactions on Multimedia.

[4]  Zhou Su,et al.  What Videos Are Similar with You?: Learning a Common Attributed Representation for Video Recommendation , 2014, ACM Multimedia.

[5]  Shao-Yuan Li,et al.  BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network , 2017, CIKM.

[6]  Jee-Hyong Lee,et al.  Collective Matrix Factorization Using Tag Embedding for Effective Recommender System , 2016, 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS).

[7]  Jian Zhao,et al.  SeqSense: Video Recommendation Using Topic Sequence Mining , 2018, MMM.

[8]  Markus Schedl,et al.  Audio-visual encoding of multimedia content for enhancing movie recommendations , 2018, RecSys.

[9]  Lars Schmidt-Thieme,et al.  Personalized Ranking for Non-Uniformly Sampled Items , 2012, KDD Cup.

[10]  Jee-Hyong Lee,et al.  Improving a recommender system by collective matrix factorization with tag information , 2014, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS).

[11]  Changsheng Xu,et al.  Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube , 2015, TOMM.

[12]  Andrei Z. Broder,et al.  Anatomy of the long tail: ordinary people with extraordinary tastes , 2010, WSDM '10.

[13]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[14]  Juntao Liu,et al.  Bayesian Probabilistic Matrix Factorization with Social Relations and Item Contents for recommendation , 2013, Decis. Support Syst..

[15]  Tat-Seng Chua,et al.  Item Silk Road: Recommending Items from Information Domains to Social Users , 2017, SIGIR.

[16]  Haoran Xie,et al.  Popularity Tendency Analysis of Ranking-Oriented Collaborative Filtering from the Perspective of Loss Function , 2014, DASFAA.

[17]  Tong Zhao,et al.  Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering , 2014, CIKM.

[18]  Steffen Rendle,et al.  Improving pairwise learning for item recommendation from implicit feedback , 2014, WSDM.

[19]  Geoffrey J. Gordon,et al.  Relational learning via collective matrix factorization , 2008, KDD.

[20]  Lixin Gao,et al.  Profiling users in a 3g network using hourglass co-clustering , 2010, MobiCom.

[21]  Xin Wang,et al.  Social Recommendation with Strong and Weak Ties , 2016, CIKM.

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

[23]  Laizhong Cui,et al.  A video recommendation algorithm based on the combination of video content and social network , 2017, Concurr. Comput. Pract. Exp..

[24]  Lars Schmidt-Thieme,et al.  Multi-relational matrix factorization using bayesian personalized ranking for social network data , 2012, WSDM '12.

[25]  Jing Ma,et al.  Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems , 2017, Knowl. Based Syst..

[26]  Yizhou Sun,et al.  Embedding for Personalized Content-based Recommendation , 2017 .

[27]  Tao Mei,et al.  Contextual Video Recommendation by Multimodal Relevance and User Feedback , 2011, TOIS.

[28]  Lejian Liao,et al.  Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking , 2017, IJCAI.

[29]  Changsheng Xu,et al.  Mining Cross-network Association for YouTube Video Promotion , 2014, ACM Multimedia.

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

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

[32]  Geoffrey J. Gordon,et al.  A Bayesian Matrix Factorization Model for Relational Data , 2010, UAI.

[33]  Alexander Tuzhilin,et al.  The long tail of recommender systems and how to leverage it , 2008, RecSys '08.

[34]  Li Chen,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering , 2022 .

[35]  Ladislav Peska.  Linking Content Information with Bayesian Personalized Ranking via Multiple Content Alignments , 2017, HT.

[36]  Cathal Gurrin,et al.  Short user-generated videos classification using accompanied audio categories , 2012, AMVA '12.

[37]  Paolo Cremonesi,et al.  Toward Building a Content-Based Video Recommendation System Based on Low-Level Features , 2015, EC-Web.

[38]  Jiangchuan Liu,et al.  Statistics and Social Network of YouTube Videos , 2008, 2008 16th Interntional Workshop on Quality of Service.

[39]  Yuchun Guo,et al.  Characterizing user popularity preference in a large-scale online video streaming system , 2015 .

[40]  Ladislav Peska.  Hybrid recommendations by content-aligned Bayesian personalized ranking , 2018, New Rev. Hypermedia Multim..

[41]  Craig MacDonald,et al.  A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation , 2017, CIKM.

[42]  George Karypis,et al.  FISM: factored item similarity models for top-N recommender systems , 2013, KDD.

[43]  Fabrice Guillemin,et al.  Experimental analysis of caching efficiency for YouTube traffic in an ISP network , 2013, Proceedings of the 2013 25th International Teletraffic Congress (ITC).

[44]  Paolo Cremonesi,et al.  Using visual features based on MPEG-7 and deep learning for movie recommendation , 2018, International Journal of Multimedia Information Retrieval.

[45]  Dietmar Jannach,et al.  Using graded implicit feedback for bayesian personalized ranking , 2014, RecSys '14.

[46]  Zhang Xiong,et al.  Item Category Aware Conditional Restricted Boltzmann Machine Based Recommendation , 2015, ICONIP.