{"type":"ScholarlyArticle","title":"JUMP: a Jointly Predictor for User Click and Dwell Time","authors":[{"name":"Tengfei Zhou","slug":"tengfei-zhou","given":"Tengfei","family":"Zhou","affiliation":"College of Computer Science and Technology , Zhejiang University ,"},{"name":"Hui Qian","slug":"hui-qian","given":"Hui","family":"Qian","affiliation":"College of Computer Science and Technology , Zhejiang University ,"},{"name":"Zebang Shen","slug":"zebang-shen","given":"Zebang","family":"Shen","affiliation":"College of Computer Science and Technology , Zhejiang University ,"},{"name":"Chao Zhang","slug":"chao-zhang","given":"Chao","family":"Zhang","affiliation":"College of Computer Science and Technology , Zhejiang University ,"},{"name":"Chengwei Wang","slug":"chengwei-wang","given":"Chengwei","family":"Wang","affiliation":"College of Computer Science and Technology , Zhejiang University ,"},{"name":"Shichen Liu","slug":"shichen-liu","given":"Shichen","family":"Liu","affiliation":"Searching Group of Alibaba Inc ."},{"name":"Wenwu Ou","slug":"wenwu-ou","given":"Wenwu","family":"Ou","affiliation":"Searching Group of Alibaba Inc ."}],"abstract":"With the recent proliferation of recommendation system, there have been a lot of interests in session-based prediction methods, particularly those based on Recurrent Neural Network (RNN) and their variants. However, existing methods either ignore the dwell time prediction that plays an important role in measuring user's engagement on the content, or fail to process very short or noisy sessions. In this paper, we propose a joint predictor, JUMP, for both user click and dwell time in session-based settings. To map its input into a feature vector, JUMP adopts a novel three-layered RNN structure which includes a fast-slow layer for very short sessions and an attention layer for noisy sessions. Experiments demonstrate that JUMP outperforms state-of-the-art methods in both user click and dwell time prediction.","doi":"10.24963/ijcai.2018/515","url":"https://ijcai.org/proceedings/2018/515","pdf":"https://ijcai.org/proceedings/2018/0515.pdf","markdown":"https://ijcai.org/proceedings/2018/515.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/515","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":515,"pages":{"first":3704,"last":3710},"keywords":["Machine Learning: Recommender Systems","Machine Learning Applications: Applications of Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv, 2014.","title":"Neural machine translation by jointly learning to align and translate","authors":"Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Baxter, 1997] Jonathan Baxter. A bayesian/information theoretic model of learning to learn via multiple task sampling. Machine learning, 28(1):7-39, 1997.","title":"A bayesian/information theoretic model of learning to learn via multiple task sampling","authors":"Jonathan Baxter","year":"1997","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bengio et al., 1994] Yoshua Bengio, Patrice Simard, and Pao- lo Frasconi. Learning long-term dependencies with gradien- t descent is difficult. IEEE transactions on neural networks, 5(2):157-166, 1994.","title":"Learning long-term dependencies with gradient descent is difficult","authors":"Yoshua Bengio, Patrice Simard, Paolo Frasconi","year":"1994","venue":"IEEE transactions on neural networks","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chung et al., 2014] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evalua- tion of gated recurrent neural networks on sequence modeling. arXiv, 2014.","title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","authors":"Chung","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dallmann et al., 2017] Alexander Dallmann, Alexander Grimm, Christian Pölitz, Daniel Zoller, and Andreas Hotho. Improving session recommendation with recurrent neural networks by ex- ploiting dwell time. arXiv, 2017.","title":"Improving session recommendation with recurrent neural networks by exploiting dwell time","authors":"Alexander Dallmann, Alexander Grimm, Christian Pölitz, Daniel Zoller, Andreas Hotho","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Du et al., 2016] Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh U- padhyay, Manuel Gomez-Rodriguez, and Le Song. Recurrent marked temporal point processes: Embedding event history to vector. In SIGKDD, pages 1555-1564. ACM, 2016.","title":"Recurrent marked temporal point processes: Embedding event history to vector","authors":"Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, Le Song","year":"2016","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hidasi and Karatzoglou, 2017] Balázs Hidasi and Alexandros Karatzoglou. Recurrent neural networks with top-k gains for session-based recommendations. arXiv, 2017.","title":"Balázs Hidasi and Alexandros Karatzoglou. Recurrent neural networks with top-k gains for session-based recommendations","authors":"Karatzoglou Hidasi","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hidasi and Tikk, 2016] Balázs Hidasi and Domonkos Tikk. Gener- al factorization framework for context-aware recommendations. Data Mining and Knowledge Discovery, 30(2):342-371, 2016.","title":"Balázs Hidasi and Domonkos Tikk. General factorization framework for context-aware recommendations","authors":"Tikk Hidasi","year":"2016","venue":"Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hidasi et al., 2015] Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based recommendations with recurrent neural networks. arXiv, 2015.","title":"Session-based recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.","title":"Long short-term memory","authors":"Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jing and Smola, 2017] How Jing and Alexander J Smola. Neural survival recommender. In WSDM, pages 515-524. ACM, 2017.","title":"How Jing and Alexander J Smola","authors":"Smola Jing","year":"2017","venue":"Neural survival recommender","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2016] Yan Li, Kevin S Xu, and Chandan K Reddy. Regu- larized parametric regression for high-dimensional survival anal- ysis. In SDM, pages 765-773. SIAM, 2016.","title":"Regularized parametric regression for high-dimensional survival analysis","authors":"Yan Li, Kevin S Xu, Chandan K Reddy","year":"2016","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2017] Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. Neural attentive session-based rec- ommendation. In CIKM, pages 1419-1428. ACM, 2017.","title":"Neural attentive session-based recommendation","authors":"Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Lin et al., 2017] Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. A structured self-attentive sentence embedding. ICLR, 2017.","title":"A structured self-attentive sentence embedding","authors":"Lin","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Linden et al., 2003] Greg Linden, Brent Smith, and Jeremy York. Amazon. com recommendations: Item-to-item collaborative fil- tering. IEEE Internet computing, 7(1):76-80, 2003.","title":"Amazon. com recommendations: Item-to-item collaborative filtering","authors":"Greg Linden, Brent Smith, Jeremy York","year":"2003","venue":"IEEE Internet computing","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2017] Yiqun Liu, Xiaohui Xie, Chao Wang, Jian-Yun Nie, Min Zhang, and Shaoping Ma. Time-aware click model. ACM Transactions on Information Systems, 35(3):16, 2017.","title":"Time-aware click model","authors":"Yiqun Liu, Xiaohui Xie, Chao Wang, Jian-Yun Nie, Min Zhang, Shaoping Ma","year":"2017","venue":"ACM Transactions on Information Systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Mujika et al., 2017] Asier Mujika, Florian Meier, and Angelika Steger. Fast-slow recurrent neural networks. In NIPS, pages 5917-5926, 2017.","title":"Fast-slow recurrent neural networks","authors":"Mujika","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Musto et al., 2015] Cataldo Musto, Giovanni Semeraro, Marco De Gemmis, and Pasquale Lops. Word embedding techniques for content-based recommender systems: An empirical evalua- tion. In RecSys, 2015.","title":"Word embedding techniques for content-based recommender systems: An empirical evaluation","authors":"Cataldo Musto, Giovanni Semeraro, Marco De Gemmis, Pasquale Lops","year":"2015","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Quadrana et al., 2017] Massimo Quadrana, Alexandros Karat- zoglou, Balázs Hidasi, and Paolo Cremonesi. Personalizing session-based recommendations with hierarchical recurrent neu- ral networks. In RecSys, pages 130-137. ACM, 2017.","title":"Personalizing session-based recommendations with hierarchical recurrent neural networks","authors":"Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi","year":"2017","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ruder, 2017] Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv, 2017.","title":"An overview of multi-task learning in deep neural networks","authors":"Sebastian Ruder","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Ruocco et al., 2017] Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, and Helge Langseth. Inter-session modeling for session- based recommendation. In Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, pages 24-31. ACM, 2017.","title":"Inter-session modeling for sessionbased recommendation","authors":"Massimiliano Ruocco, Ole Steinar Lillestøl, Helge Skrede, Langseth","year":"2017","venue":"Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Shani et al., 2005] Guy Shani, David Heckerman, and Ronen I Brafman. An mdp-based recommender system. JMLR, 6(Sep):1265-1295, 2005.","title":"An mdp-based recommender system","authors":"Guy Shani, David Heckerman, Ronen I Brafman","year":"2005","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Suarez, 2017] Joseph Suarez. Language modeling with recurrent highway hypernetworks. In NIPS, pages 3269-3278, 2017.","title":"Language modeling with recurrent highway hypernetworks","authors":"Joseph Suarez","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Sukhbaatar et al., 2015] Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al. End-to-end memory networks. In NIPS, pages 2440-2448, 2015.","title":"End-to-end memory networks","authors":"Sukhbaatar","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Tavakol and Brefeld, 2014] Maryam Tavakol and Ulf Brefeld. Factored mdps for detecting topics of user sessions. In RecSys, pages 33-40. ACM, 2014.","title":"Maryam Tavakol and Ulf Brefeld. Factored mdps for detecting topics of user sessions","authors":"Brefeld Tavakol","year":"2014","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wang et al., 2017] Ping Wang, Yan Li, and Chandan K Reddy. Machine learning for survival analysis: A survey. arXiv, 2017.","title":"Machine learning for survival analysis: A survey","authors":"Ping Wang, Yan Li, Chandan K Reddy","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wu et al., 2016a] Sai Wu, Weichao Ren, Chengchao Yu, Gang Chen, Dongxiang Zhang, and Jingbo Zhu. Personal recommen- dation using deep recurrent neural networks in netease. In ICDE, pages 1218-1229. IEEE, 2016.","title":"Personal recommendation using deep recurrent neural networks in netease","authors":"Wu","year":"2016","venue":"ICDE","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wu et al., 2016b] Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, and Ruslan R Salakhutdinov. On multiplicative integration with recurrent neural networks. In NIPS, pages 2856- 2864, 2016.","title":"On multiplicative integration with recurrent neural networks","authors":"Wu","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Xiao et al., 2017] Shuai Xiao, Junchi Yan, Mehrdad Farajtabar, Le Song, Xiaokang Yang, and Hongyuan Zha. Joint modeling of event sequence and time series with attentional twin recurrent neural networks. arXiv, 2017.","title":"Joint modeling of event sequence and time series with attentional twin recurrent neural networks","authors":"Shuai Xiao, Junchi Yan, Mehrdad Farajtabar, Le Song, Xiaokang Yang, Hongyuan Zha","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Yi et al., 2014] Xing Yi, Liangjie Hong, Erheng Zhong, Nan- than Nan Liu, and Suju Rajan. Beyond clicks: dwell time for personalization. In RecSys, pages 113-120. ACM, 2014.","title":"Beyond clicks: dwell time for personalization","authors":"Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan, Nan Liu, Suju Rajan","year":"2014","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Yin et al., 2013] Peifeng Yin, Ping Luo, Wang-Chien Lee, and Min Wang. Silence is also evidence: interpreting dwell time for recommendation from psychological perspective. In KDD, pages 989-997. ACM, 2013.","title":"Silence is also evidence: interpreting dwell time for recommendation from psychological perspective","authors":"Peifeng Yin, Ping Luo, Wang-Chien Lee, Min Wang","year":"2013","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Zhang et al., 2014] Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, and Tie-Yan Liu. Sequential click prediction for sponsored search with recurrent neural networks. In AAAI, volume 14, pages 1369-1375, 2014.","title":"Sequential click prediction for sponsored search with recurrent neural networks","authors":"Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zilly et al., 2016] Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, and Jürgen Schmidhuber. Recurrent highway net- works. arXiv, 2016.","title":"Recurrent highway networks","authors":"Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}