Abstract
E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well known that there are two types of preferences: long-term ones and short-term ones. The former refers to users’ inherent purchasing bias and evolves slowly. By contrast, the latter reflects users’ purchasing inclination in a relatively short period. They both affect users’ current purchasing intentions. However, few research efforts have been dedicated to jointly model them for the personalized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long- and short-term user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the short-term as well as long-term user preferences are more relevant to the current query. This unique design enables our model to capture users’ current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long- and short-term user preferences with the given query for the personalized search. Extensive experiments over four Amazon product datasets show that our model significantly outperforms several state-of-the-art product search methods in terms of different evaluation metrics.
- Qingyao Ai, Yongfeng Zhang, Keping Bi, Xu Chen, and W. Bruce Croft. 2017. Learning a hierarchical embedding model for personalized product search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 645--654. Google ScholarDigital Library
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. In arXiv preprint arXiv:1409.0473.Google Scholar
- Paul N. Bennett, Ryen W. White, Wei Chu, Susan T. Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012. Modeling the impact of short-and long-term behavior on search personalization. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 185--194. Google ScholarDigital Library
- Alexey Borisov, Ilya Markov, Maarten de Rijke, and Pavel Serdyukov. 2016. A neural click model for web search. In Proceedings of the 25th International Conference on World Wide Web. ACM, 531--541. Google ScholarDigital Library
- Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 335--344. Google ScholarDigital Library
- Long Chen, Hanwang Zhang, Jun Xiao, Liqiang Nie, Jian Shao, Wei Liu, and Tat-Seng Chua. 2017. Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). IEEE, 6298--6306.Google ScholarCross Ref
- Shiqian Chen, Chenliang Li, Feng Ji, Wei Zhou, and Haiqing Chen. 2019. Review-driven answer generation for product-related questions in E-Commerce. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM.Google ScholarDigital Library
- Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, and Mohan Kankanhalli. 2018. MMALFM: Explainable recommendation by leveraging reviews and images. arXiv preprint arXiv:1811.05318. Google ScholarDigital Library
- Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, and Mohan S. Kankanhalli. 2018. Aˆ 3NCF: An adaptive aspect attention model for rating prediction. In Proceedings of the International Joint Conference on Artificial Intelligence. AAAI, 3748--3754. Google ScholarDigital Library
- Zhiyong Cheng, Ying Ding, Lei Zhu, and Mohan Kankanhalli. 2018. Aspect-Aware latent factor model: Rating prediction with ratings and reviews. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. ACM, 639--648. Google ScholarDigital Library
- Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder--decoder approaches. In Syntax, Semantics and Structure in Statistical Translation (2014), 103.Google ScholarCross Ref
- Sung H. Chung, Anjan Goswami, Honglak Lee, and Junling Hu. 2012. The impact of images on user clicks in product search. In Proceedings of the 12th International Workshop on Multimedia Data Mining. ACM, 25--33. Google ScholarDigital Library
- Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (elus). In arXiv preprint arXiv:1511.07289.Google Scholar
- Mariam Daoud, Lynda Tamine-Lechani, and Mohand Boughanem. 2008. Learning user interests for a session-based personalized search. In Proceedings of the 2nd International Symposium on Information Interaction in Context. ACM, 57--64. Google ScholarDigital Library
- Mariam Daoud, Lynda Tamine-Lechani, Mohand Boughanem, and Bilal Chebaro. 2009. A session based personalized search using an ontological user profile. In Proceedings of the 2009 ACM Symposium on Applied Computing. ACM, 1732--1736. Google ScholarDigital Library
- Atish Das Sarma, Nish Parikh, and Neel Sundaresan. 2014. E-commerce product search: Personalization, diversification, and beyond. In Proceedings of the 23rd International Conference on World Wide Web. ACM, 189--190. Google ScholarDigital Library
- Huizhong Duan and ChengXiang Zhai. 2015. Mining coordinated intent representation for entity search and recommendation. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management. ACM, 333--342. Google ScholarDigital Library
- Huizhong Duan, ChengXiang Zhai, Jinxing Cheng, and Abhishek Gattani. 2013. A probabilistic mixture model for mining and analyzing product search log. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. ACM, 2179--2188. Google ScholarDigital Library
- Huizhong Duan, ChengXiang Zhai, Jinxing Cheng, and Abhishek Gattani. 2013. Supporting keyword search in product database: A probabilistic approach. Proc. VLDB Endow. 6, 14 (2013), 1786--1797. Google ScholarDigital Library
- Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. MIT, 249--256.Google Scholar
- Anjan Goswami, Naren Chittar, and Chung H. Sung. 2011. A study on the impact of product images on user clicks for online shopping. In Proceedings of the 20th International Conference Companion on World Wide Web. ACM, 45--46. Google ScholarDigital Library
- Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016. A deep relevance matching model for ad-hoc retrieval. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management. ACM, 55--64. Google ScholarDigital Library
- Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Xin-Shun Xu, and Mohan Kankanhalli. 2018. Multi-modal preference modeling for product search. In Proceedings of the 2018 ACM Multimedia Conference on Multimedia Conference. ACM, 1865--1873. Google ScholarDigital Library
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web. ACM, 507--517. Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. ACM, 173--182. Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. In arXiv preprint arXiv:1511.06939.Google Scholar
- Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. In Advances in Neural Information Processing Systems. MIT, 2042--2050. Google ScholarDigital Library
- Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. ACM, 2333--2338. Google ScholarDigital Library
- Ameni Kacem, Mohand Boughanem, and Rim Faiz. 2017. Emphasizing temporal-based user profile modeling in the context of session search. In Proceedings of the Symposium on Applied Computing. ACM, 925--930. Google ScholarDigital Library
- Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of the International Conference on Machine Learning. ACM, 1188--1196. Google ScholarDigital Library
- Beibei Li, Anindya Ghose, and Panagiotis G. Ipeirotis. 2011. Towards a theory model for product search. In Proceedings of the 20th International Conference on World Wide Web. ACM, 327--336. Google ScholarDigital Library
- Chenliang Li, Yu Duan, Haoran Wang, Zhiqian Zhang, Aixin Sun, and Zongyang Ma. 2017. Enhancing topic modeling for short texts with auxiliary word embeddings. ACM Trans. Inf. Syst. 36, 2 (2017), 11. Google ScholarDigital Library
- Chenliang Li, Haoran Wang, Zhiqian Zhang, Aixin Sun, and Zongyang Ma. 2016. Topic modeling for short texts with auxiliary word embeddings. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 165--174. Google ScholarDigital Library
- Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 1419--1428. Google ScholarDigital Library
- Sheng Li, Jaya Kawale, and Yun Fu. 2015. Deep collaborative filtering via marginalized denoising auto-encoder. In Proceedings of the 24th ACM International on Information and Knowledge Management. ACM, 811--820. Google ScholarDigital Library
- Nicolaas Matthijs and Filip Radlinski. 2011. Personalizing web search using long term browsing history. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. ACM, 25--34. Google ScholarDigital Library
- Julian McAuley, Rahul Pandey, and Jure Leskovec. 2015. Inferring networks of substitutable and complementary products. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 785--794. Google ScholarDigital Library
- Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 43--52. Google ScholarDigital Library
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In arXiv preprint arXiv:1301.3781.Google Scholar
- Joao Palotti. 2016. Learning to Rank for Personalized e-commerce Search at CIKM Cup 2016. Technical Report. Tech. rep.Google Scholar
- Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. 2016. A study of matchpyramid models on ad-hoc retrieval. In arXiv preprint arXiv:1606.04648.Google Scholar
- Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Jingfang Xu, and Xueqi Cheng. 2017. DeepRank: A new deep architecture for relevance ranking in information retrieval. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 257--266. Google ScholarDigital Library
- Nish Parikh and Neel Sundaresan. 2011. Beyond relevance in marketplace search. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM, 2109--2112. Google ScholarDigital Library
- Nish Parikh and Neel Sundaresan. 2011. A user-tunable approach to marketplace search. In Proceedings of the 20th International Conference Companion on World Wide Web. ACM, 245--248. Google ScholarDigital Library
- Seung-Taek Park and David M. Pennock. 2007. Applying collaborative filtering techniques to movie search for better ranking and browsing. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 550--559. Google ScholarDigital Library
- Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013. On the difficulty of training recurrent neural networks. In Proceedings of the International Conference on Machine Learning. ACM, 1310--1318. Google ScholarDigital Library
- Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, and Chenliang Li. 2017. NeuPL: Attention-based semantic matching and pair-linking for entity disambiguation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 1667--1676. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. AUAI, 452--461. Google ScholarDigital Library
- Matthew Richardson. 2008. Learning about the world through long-term query logs. ACM Trans. Web 2, 4 (2008), 21. Google ScholarDigital Library
- Jennifer Rowley. 2000. Product search in e-shopping: A review and research propositions. J. Cons. Market. 17, 1 (2000), 20--35.Google ScholarCross Ref
- Aliaksei Severyn and Alessandro Moschitti. 2015. Learning to rank short text pairs with convolutional deep neural networks. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 373--382. Google ScholarDigital Library
- Xuehua Shen, Bin Tan, and ChengXiang Zhai. 2005. Implicit user modeling for personalized search. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management. ACM, 824--831. Google ScholarDigital Library
- Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014. Learning semantic representations using convolutional neural networks for web search. In Proceedings of the 23rd International Conference on World Wide Web. ACM, 373--374. Google ScholarDigital Library
- David Sontag, Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Susan Dumais, and Bodo Billerbeck. 2012. Probabilistic models for personalizing web search. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining. ACM, 433--442. Google ScholarDigital Library
- Smitha Sriram, Xuehua Shen, and Chengxiang Zhai. 2004. A session-based search engine. In Proceedings of the 27th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 492--493. Google ScholarDigital Library
- Bin Tan, Xuehua Shen, and ChengXiang Zhai. 2006. Mining long-term search history to improve search accuracy. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 718--723. Google ScholarDigital Library
- Imad Tbahriti, Christine Chichester, Frédérique Lisacek, and Patrick Ruch. 2006. Using argumentation to retrieve articles with similar citations: An inquiry into improving related articles search in the MEDLINE digital library. Int. J. Med. Inform. 75, 6 (2006), 488--495.Google ScholarCross Ref
- Yury Ustinovskiy and Pavel Serdyukov. 2013. Personalization of web-search using short-term browsing context. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. ACM, 1979--1988. Google ScholarDigital Library
- Christophe Van Gysel, Maarten de Rijke, and Evangelos Kanoulas. 2016. Learning latent vector spaces for product search. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management. ACM, 165--174. Google ScholarDigital Library
- Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, Liang Pang, and Xueqi Cheng. 2016. Match-srnn: Modeling the recursive matching structure with spatial rnn. In Proceedings of the International Joint Conference on Artificial Intelligence. AAAI, 2922--2928. Google ScholarDigital Library
- Ryen W. White, Paul N. Bennett, and Susan T. Dumais. 2010. Predicting short-term interests using activity-based search context. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, 1009--1018. Google ScholarDigital Library
- Chen Wu, Ming Yan, and Luo Si. 2017. Ensemble methods for personalized e-commerce search challenge at CIKM Cup 2016. In arXiv preprint arXiv:1708.04479.Google Scholar
- Liang Xiang, Quan Yuan, Shiwan Zhao, Li Chen, Xiatian Zhang, Qing Yang, and Jimeng Sun. 2010. Temporal recommendation on graphs via long-and short-term preference fusion. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 723--732. Google ScholarDigital Library
- Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017. Attentional factorization machines: Learning the weight of feature interactions via attention networks. In Proceedings of the International Joint Conference on Artificial Intelligence. AAAI. Google ScholarDigital Library
- Huijuan Xu and Kate Saenko. 2016. Ask, attend and answer: Exploring question-guided spatial attention for visual question answering. In Proceedings of the European Conference on Computer Vision. Springer, 451--466.Google ScholarCross Ref
- Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the International Conference on Machine Learning. ACM, 2048--2057. Google ScholarDigital Library
- Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola. 2016. Stacked attention networks for image question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 21--29.Google ScholarCross Ref
- Jun Yu, Sunil Mohan, Duangmanee Pew Putthividhya, and Weng-Keen Wong. 2014. Latent dirichlet allocation based diversified retrieval for e-commerce search. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM, 463--472. Google ScholarDigital Library
- Chengxiang Zhai and John Lafferty. 2004. A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 22, 2 (2004), 179--214. Google ScholarDigital Library
- Hanwang Zhang, Yang Yang, Huanbo Luan, Shuicheng Yang, and Tat-Seng Chua. 2014. Start from scratch: Towards automatically identifying, modeling, and naming visual attributes. In Proceedings of the 22nd ACM International Conference on Multimedia. ACM, 187--196. Google ScholarDigital Library
- Liron Zighelnic and Oren Kurland. 2008. Query-drift prevention for robust query expansion. In Proceedings of the 31st International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 825--826. Google ScholarDigital Library
Index Terms
- Attentive Long Short-Term Preference Modeling for Personalized Product Search
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