{"type":"ScholarlyArticle","title":"Your Tweets Reveal What You Like: Introducing Cross-media Content Information into Multi-domain Recommendation","authors":[{"name":"Weizhi Ma","slug":"weizhi-ma","given":"Weizhi","family":"Ma","affiliation":"Beijing National Research Center for Information Science and Technology , Tsinghua University"},{"name":"Min Zhang","slug":"min-zhang","given":"Min","family":"Zhang","affiliation":"Beijing National Research Center for Information Science and Technology , Tsinghua University"},{"name":"Chenyang Wang","slug":"chenyang-wang","given":"Chenyang","family":"Wang","affiliation":"Beijing National Research Center for Information Science and Technology , Tsinghua University"},{"name":"Cheng Luo","slug":"cheng-luo","given":"Cheng","family":"Luo","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China"},{"name":"Yiqun Liu","slug":"yiqun-liu","given":"Yiqun","family":"Liu","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China"},{"name":"Shaoping Ma","slug":"shaoping-ma","given":"Shaoping","family":"Ma","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China"}],"abstract":"Cold start is a challenging problem in recommender systems. Many previous studies attempt to utilize extra information from other platforms to alleviate the problem. Most of the leveraged information is on-topic, directly related to users' preferences in the target domain. Thought to be unrelated, users' off-topic content information (such as user tweets) is usually omitted. However, the off-topic content information also helps to indicate the similarity of users on their tastes, interests, and opinions, which matches the underlying assumption of Collaborative Filtering (CF) algorithms. In this paper, we propose a framework to capture the features from user's off-topic content information in social media and introduce them into Matrix Factorization (MF) based algorithms. The framework is easy to understand and flexible in different embedding approaches and MF based algorithms. To the best of our knowledge, there is no previous study in which user's off-topic content in other platforms is taken into consideration. By capturing the cross-platform content including both on-topic and off-topic information, multiple algorithms with several embedding learning approaches have achieved significant improvements in rating prediction on three datasets. Especially in cold start scenarios, we observe greater enhancement.  The results confirm our suggestion that off-topic cross-media information also contributes to the recommendation.","doi":"10.24963/ijcai.2018/484","url":"https://ijcai.org/proceedings/2018/484","pdf":"https://ijcai.org/proceedings/2018/0484.pdf","markdown":"https://ijcai.org/proceedings/2018/484.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/484","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":484,"pages":{"first":3484,"last":3490},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Ai et al., 2016] Qingyao Ai, Liu Yang, Jiafeng Guo, and W. Bruce Croft. Improving language estimation with the paragraph vector model for ad-hoc retrieval. In Interna- tional ACM SIGIR Conference on Research and Develop- ment in Information Retrieval, pages 869-872, 2016.","title":"Improving language estimation with the paragraph vector model for ad-hoc retrieval","authors":"Ai","year":"2016","venue":"International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Beutel et al., 2014] Alex Beutel, Kenton Murray, Christos Faloutsos, and Alexander J Smola. Cobafi: collabora- tive bayesian filtering. In Proceedings of the 23rd inter- national conference on World wide web, pages 97-108. ACM, 2014.","title":"Cobafi: collaborative bayesian filtering","authors":"Alex Beutel, Kenton Murray, Christos Faloutsos, Alexander J Smola","year":"2014","venue":"Proceedings of the 23rd international conference on World wide web","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Blei et al., 2003] David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993-1022, 2003.","title":"Latent dirichlet allocation","authors":"Andrew Y David M Blei, Michael I Ng, Jordan","year":"2003","venue":"Journal of machine Learning research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Deng et al., 2013] Zhengyu Deng, Jitao Sang, and Chang- sheng Xu. Personalized video recommendation based on cross-platform user modeling. In Multimedia and Expo (ICME), pages 1-6. IEEE, 2013.","title":"Personalized video recommendation based on cross-platform user modeling","authors":"Zhengyu Deng, Jitao Sang, Changsheng Xu","year":"2013","venue":"Multimedia and Expo (ICME)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Diao et al., 2014] Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J Smola, Jing Jiang, and Chong Wang. Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars). In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discov- ery and data mining, pages 193-202. ACM, 2014.","title":"Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars)","authors":"Qiming Diao, Minghui Qiu, Chao-Yuan, Alexander J Wu, Jing Smola, Chong Jiang, Wang","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Elkahky et al., 2015] Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. A multi-view deep learning approach for cross domain user modeling in recommendation sys- tems. In Proceedings of the 24th International Conference on World Wide Web, pages 278-288. International World Wide Web Conferences Steering Committee, 2015.","title":"A multi-view deep learning approach for cross domain user modeling in recommendation systems","authors":"Ali Mamdouh, Elkahky, Yang Song, Xiaodong He","year":"2015","venue":"Proceedings of the 24th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[He and Chua, 2017] Xiangnan He and Tat-Seng Chua. Neu- ral factorization machines for sparse predictive analytics. 2017.","title":"Xiangnan He and Tat-Seng Chua. Neural factorization machines for sparse predictive analytics","authors":"Chua He","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Jiang et al., 2016] Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, and Shiqiang Yang. Little is much: Bridg- ing cross-platform behaviors through overlapped crowds. In AAAI, pages 13-19, 2016.","title":"Little is much: Bridging cross-platform behaviors through overlapped crowds","authors":"Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, Shiqiang Yang","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Le and Mikolov, 2014] Quoc Le and Tomas Mikolov. Dis- tributed representations of sentences and documents. In Proceedings of the 31st International Conference on Ma- chine Learning (ICML-14), pages 1188-1196, 2014.","title":"Quoc Le and Tomas Mikolov. Distributed representations of sentences and documents","authors":"Mikolov Le","year":"2014","venue":"Proceedings of the 31st International Conference on Machine Learning (ICML-14)","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Man et al., 2017] Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. Cross-domain recommendation: an em- bedding and mapping approach. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pages 2464-2470. AAAI Press, 2017.","title":"Cross-domain recommendation: an embedding and mapping approach","authors":"Huawei Tong Man, Xiaolong Shen, Xueqi Jin, Cheng","year":"2017","venue":"Proceedings of the 26th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2017/343"},{"position":11,"raw":"[McAuley and Leskovec, 2013] Julian McAuley and Jure Leskovec. Hidden factors and hidden topics: understand- ing rating dimensions with review text. In Proceedings of the 7th ACM conference on Recommender systems, pages 165-172. ACM, 2013.","title":"Hidden factors and hidden topics: understanding rating dimensions with review text","authors":"Leskovec Mcauley, Julian Mcauley, Jure Leskovec","year":"2013","venue":"Proceedings of the 7th ACM conference on Recommender systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Kai Chen, Greg Cor- rado, and Jeffrey Dean. Efficient estimation of word rep- resentations in vector space. Computer Science, 2013. [Mnih and Salakhutdinov, 2008] Andriy Mnih and Ruslan R Salakhutdinov. Probabilistic matrix factorization. In Ad- vances in neural information processing systems, pages 1257-1264, 2008.","title":"Efficient estimation of word representations in vector space","authors":"Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean","year":"2008","venue":"Andriy Mnih and Ruslan R Salakhutdinov. Probabilistic matrix factorization. In Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Porteous et al., 2008] Ian Porteous, Evgeniy Bart, and Max Welling. Multi-hdp: A non parametric bayesian model for tensor factorization. In Aaai, volume 8, pages 1487-1490, 2008.","title":"Multi-hdp: A non parametric bayesian model for tensor factorization","authors":"Ian Porteous, Evgeniy Bart, Max Welling","year":"2008","venue":"Aaai","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Rennie and Srebro, 2005] Jasson DM Rennie and Nathan Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22nd inter- national conference on Machine learning, pages 713-719. ACM, 2005.","title":"Fast maximum margin matrix factorization for collaborative prediction","authors":"Rennie, Srebro ; ] Jasson, D M Rennie, Nathan Srebro","year":"2005","venue":"Proceedings of the 22nd international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Vincent et al., 2010] Pascal Vincent, Hugo Larochelle, Is- abelle Lajoie, Yoshua Bengio, and Pierre-Antoine Man- zagol. Stacked denoising autoencoders: Learning use- ful representations in a deep network with a local de- noising criterion. Journal of Machine Learning Research, 11(Dec):3371-3408, 2010.","title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","authors":"Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol","year":"2010","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Wang et al., 2017a] Xiang Wang, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. Item silk road: Recommend- ing items from information domains to social users. arXiv preprint arXiv:1706.03205, 2017.","title":"Item silk road: Recommending items from information domains to social users","authors":"Wang","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang et al., 2017b] Xin Wang, Steven CH Hoi, Martin Es- ter, Jiajun Bu, and Chun Chen. Learning personalized preference of strong and weak ties for social recommen- dation. In Proceedings of the 26th International Confer- ence on World Wide Web, pages 1601-1610. International World Wide Web Conferences Steering Committee, 2017.","title":"Learning personalized preference of strong and weak ties for social recommendation","authors":"Wang","year":"2017","venue":"Proceedings of the 26th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Yan et al., 2015] Ming Yan, Jitao Sang, and Changsheng Xu. Unified youtube video recommendation via cross- network collaboration. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pages 19-26. ACM, 2015.","title":"Unified youtube video recommendation via crossnetwork collaboration","authors":"Ming Yan, Jitao Sang, Changsheng Xu","year":"2015","venue":"Proceedings of the 5th ACM on International Conference on Multimedia Retrieval","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Yang et al., 2013] Bo Yang, Yu Lei, Dayou Liu, and Jiming Liu. Social collaborative filtering by trust. In International Joint Conference on Artificial Intelligence, pages 2747- 2753, 2013.","title":"Social collaborative filtering by trust","authors":"Bo Yang, Yu Lei, Dayou Liu, Jiming Liu","year":"2013","venue":"International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2013/404"},{"position":20,"raw":"[Yuan et al., 2013] Nicholas Jing Yuan, Fuzheng Zhang, Defu Lian, Kai Zheng, Siyu Yu, and Xing Xie. We know how you live: exploring the spectrum of urban lifestyles. In Proceedings of the first ACM conference on Online so- cial networks, pages 3-14. ACM, 2013.","title":"We know how you live: exploring the spectrum of urban lifestyles","authors":"Nicholas Jing Yuan, Fuzheng Zhang, Defu Lian, Kai Zheng, Siyu Yu, Xing Xie","year":"2013","venue":"Proceedings of the first ACM conference on Online social networks","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhang et al., 2016a] Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international con- ference on knowledge discovery and data mining, pages 353-362. ACM, 2016.","title":"Collaborative knowledge base embedding for recommender systems","authors":"Zhang","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhang et al., 2016b] Zihan Zhang, Xiaoming Jin, Lianghao Li, Guiguang Ding, and Qiang Yang. Multi-domain active learning for recommendation. In AAAI, pages 2358-2364, 2016.","title":"Multi-domain active learning for recommendation","authors":"Zhang","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}