Yuqi Qin
ABSTRACT
The query-based recommendation now is becoming a basic research topic in the e-commerce scenario. Generally, given a query that a user typed, it aims to provide a set of items that the user may be interested in. In this task, the customer intention (i.e., browsing or purchase) is an important factor to configure the corresponding recommendation strategy for better shopping experiences (i.e., providing diverse items when the user prefers to browse or recommending specific items when detecting the user is willing to purchase). Though necessary, this is usually overlooked in previous works. In addition, the diversity and evolution of user interests also bring challenges to inferring user intentions correctly.
In this paper, we propose a predecessor task to infer two important customer intentions, which are purchasing and browsing respectively, and we introduce a novel Psychological Intention Prediction Model (PIPM for short) to address this issue. Inspired by cognitive psychology, we first devise a multi-interest extraction module to adaptively extract interests from the user-item interaction sequence. After this, we design an interest evolution layer to model the evolution of the mined multiple interests. Finally, we aggregate all evolved multiple interests to infer users' intentions in his/her next visit. Extensive experiments are conducted on a large-scale Taobao industrial dataset. The results demonstrate that PIPM gains a significant improvement on AUC and GAUC than state-of-the-art baselines. Notably, PIPM has been deployed on the Taobao e-commerce platform and obtained over 10% improvement on PCTR.
- Christos George Bampis, Cristian Rusu, Hazem M. Hajj, and Alan C. Bovik. 2017. Robust matrix factorization for collaborative filtering in recommender systems. In 51st Asilomar Conference on Signals, Systems, and Computers, ACSSC 2017, Pacific Grove, CA, USA, October 29 - November 1, 2017,, Michael B. Matthews (Ed.). IEEE, 415--419. https://doi.org/10.1109/ACSSC.2017.8335371Google Scholar
Cross Ref
- Amin Beheshti, Shahpar Yakhchi, Salman Mousaeirad, Seyed Mohssen Ghafari, Srinivasa Reddy Goluguri, and Mohammad Amin Edrisi. 2020. Towards Cognitive Recommender Systems. Algorithms, Vol. 13, 8 (2020). https://doi.org/10.3390/a13080176Google Scholar
- Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. 2020. Controllable Multi-Interest Framework for Recommendation. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020, Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 2942--2951. https://doi.org/10.1145/3394486.3403344Google ScholarDigital Library
- Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, and Yongliang Li. 2019. Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019. ACM, 2478--2486. https://doi.org/10.1145/3292500.3330673Google ScholarDigital Library
- E.B. Goldstein. 2014. Cognitive Psychology: Connecting Mind, Research and Everyday Experience. Cengage Learning. https://books.google.com/books?id=4LI8AwAAQBAJGoogle Scholar
- Cheng Guo, Mengfei Zhang, Jinyun Fang, Jiaqi Jin, and Mao Pan. 2020. Session-based Recommendation with Hierarchical Leaping Networks. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 1705--1708. https://doi.org/10.1145/3397271.3401217Google ScholarDigital Library
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017,, Carles Sierra (Ed.). ijcai.org, 1725--1731. https://doi.org/10.24963/ijcai.2017/239Google ScholarCross Ref
- Long Guo, Lifeng Hua, Rongfei Jia, Binqiang Zhao, Xiaobo Wang, and Bin Cui. 2019. Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019. ACM, 1984--1992. https://doi.org/10.1145/3292500.3330670Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings.Google Scholar
- Wang-Cheng Kang and Julian J. McAuley. 2018. Self-Attentive Sequential Recommendation. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17-20, 2018. 197--206.Google Scholar
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer, Vol. 42, 8 (2009), 30--37. https://doi.org/10.1109/MC.2009.263Google ScholarDigital Library
- Duc-Trong Le, Hady Wirawan Lauw, and Yuan Fang. 2017. Basket-Sensitive Personalized Item Recommendation. In IJCAI 19--25, 2017. 2060--2066.Google Scholar
- Duc-Trong Le, Hady W. Lauw, and Yuan Fang. 2019. Correlation-Sensitive Next-Basket Recommendation. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019. 2808--2814.Google ScholarCross Ref
- Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019. Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019. ACM, 2615--2623. https://doi.org/10.1145/3357384.3357814Google ScholarDigital Library
- Hao Li, Kenli Li, Ji-yao An, and Keqin Li. 2018. MSGD: A Novel Matrix Factorization Approach for Large-Scale Collaborative Filtering Recommender Systems on GPUs. IEEE Trans. Parallel Distributed Syst., Vol. 29, 7 (2018), 1530--1544. https://doi.org/10.1109/TPDS.2017.2718515Google ScholarDigital Library
- Jiacheng Li, Yujie Wang, and Julian J. McAuley. 2020. Time Interval Aware Self-Attention for Sequential Recommendation. In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020, James Caverlee, Xia (Ben) Hu, Mounia Lalmas, and Wei Wang (Eds.). ACM, 322--330. https://doi.org/10.1145/3336191.3371786Google ScholarDigital Library
- Zhiwei Liu, Ziwei Fan, Yu Wang, and Philip S. Yu. 2021. Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021. ACM, 1608--1612. https://doi.org/10.1145/3404835.3463036Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web - WWW '10. ACM Press, New York, New York, USA, 811--820. https://doi.org/10.1145/1772690.1772773Google ScholarDigital Library
- Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the Tenth International World Wide Web Conference, WWW 10, Hong Kong, China, May 1-5, 2001, Vincent Y. Shen, Nobuo Saito, Michael R. Lyu, and Mary Ellen Zurko (Eds.). ACM, 285--295. https://doi.org/10.1145/371920.372071Google ScholarDigital Library
- Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019, Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu (Eds.). ACM, 1441--1450. https://doi.org/10.1145/3357384.3357895Google ScholarDigital Library
- Leilei Sun, Yansong Bai, Bowen Du, Chuanren Liu, Hui Xiong, and Weifeng Lv. 2020. Dual Sequential Network for Temporal Sets Prediction. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 1439--1448. https://doi.org/10.1145/3397271.3401124Google ScholarDigital Library
- Panagiotis Symeonidis and Andreas Zioupos. 2016. Matrix and Tensor Factorization Techniques for Recommender Systems. Springer. https://doi.org/10.1007/978-3-319-41357-0Google Scholar
- Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, and Xia Hu. 2021. Sparse-Interest Network for Sequential Recommendation. In WSDM '21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021, Liane Lewin-Eytan, David Carmel, Elad Yom-Tov, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 598--606. https://doi.org/10.1145/3437963.3441811Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA,, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998--6008. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.htmlGoogle ScholarDigital Library
- Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning Hierarchical Representation Model for NextBasket Recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, August 9-13, 2015. 403--412.Google ScholarDigital Library
- Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, and Wei Liu. 2018. Attention-Based Transactional Context Embedding for Next-Item Recommendation. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2019. 2532--2539.Google ScholarCross Ref
- Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential Recommender System based on Hierarchical Attention Networks. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden,, Jérôme Lang (Ed.). ijcai.org, 3926--3932. https://doi.org/10.24963/ijcai.2018/546Google ScholarCross Ref
- Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. A Dynamic Recurrent Model for Next Basket Recommendation. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016. 729--732.Google ScholarDigital Library
- Kai Zhang, Hao Qian, Qing Cui, Qi Liu, Longfei Li, Jun Zhou, Jianhui Ma, and Enhong Chen. 2021. Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction. In WSDM '21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021. ACM, 984--992. https://doi.org/10.1145/3437963.3441761Google ScholarDigital Library
- Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep Interest Evolution Network for Click-Through Rate Prediction. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press, 5941--5948. https://doi.org/10.1609/aaai.v33i01.33015941Google ScholarDigital Library
- Guorui Zhou, Xiaoqiang Zhu, Chengru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2019,, Yike Guo and Faisal Farooq (Eds.). ACM, 1059--1068. https://doi.org/10.1145/3219819.3219823Google ScholarDigital Library
- Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020. S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020. ACM, 1893--1902. https://doi.org/10.1145/3340531.3411954Google ScholarDigital Library
- Nengjun Zhu, Jian Cao, Yanchi Liu, Yang Yang, Haochao Ying, and Hui Xiong. 2020. Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation. In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020, James Caverlee, Xia (Ben) Hu, Mounia Lalmas, and Wei Wang (Eds.). ACM, 807--815. https://doi.org/10.1145/3336191.3371840Google ScholarDigital Library
Index Terms
- A Multi-Interest Evolution Story: Applying Psychology in Query-based Recommendation for Inferring Customer Intention
Recommendations
Generating Top-N Items Recommendation Set Using Collaborative, Content Based Filtering and Rating Variance
AbstractThe main purpose of any recommendation system is to recommend items of users’ interest. Mostly content and collaborative filtering are widely used recommendation systems. Matrix factorization technique is also used by many recommendation systems. ...
Mining User Interest Change for Improving Collaborative Filtering
IITA '08: Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 03Collaborative filtering recommendation system is a widely used method of providing recommendations using explicit ratings on items from users, which provides personalized recommendations on products or services to customers. However, the current ...
A Hybrid Collaborative Filtering Algorithm Based on User-Item
ICCIS '10: Proceedings of the 2010 International Conference on Computational and Information SciencesCollaborative filtering is one of the most important technologies in e-commerce recommendation system. Traditional similarity measure methods work poorly when the user rating data are extremely sparse. Aiming at this issue a hybrid collaborative ...
Comments