ABSTRACT
Product search forms an indispensable component of any e-commerce service, and helps customers find products of their interest from a large catalog on these websites. When products that are irrelevant to the search query are surfaced, it leads to a poor customer experience, thus reducing user trust and increasing the likelihood of churn. While identifying and removing such results from product search is crucial, doing so is a burdensome task that requires large amounts of human annotated data to train accurate models. This problem is exacerbated when products are cross-listed across countries that speak multiple languages, and customers specify queries in multiple languages and from different cultural contexts. In this work, we propose a novel multi-lingual multi-task learning framework, to jointly train product search models on multiple languages, with limited amount of training data from each language. By aligning the query and product representations from different languages into a language-independent vector space of queries and products, respectively, the proposed model improves the performance over baseline search models in any given language. We evaluate the performance of our model on real data collected from a leading e-commerce service. Our experimental evaluation demonstrates up to 23% relative improvement in the classification F1-score compared to the state-of-the-art baseline models.
- Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et almbox. 2016. Tensorflow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). 265--283.Google ScholarDigital Library
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. International Conference on Lerning Representations (ICLR) (2015).Google Scholar
- Lisa Ballesteros and Bruce Croft. 1996. Dictionary methods for cross-lingual information retrieval. In International Conference on Database and Expert Systems Applications. Springer, 791--801.Google ScholarCross Ref
- Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, and W Bruce Croft. 2019. Leverage Implicit Feedback for Context-aware Product Search. (2019).Google Scholar
- Johannes Bjerva and Robert Östling. 2017. Cross-lingual learning of semantic textual similarity with multilingual word representations. In 21st Nordic Conference on Computational Linguistics, NoDaLiDa, Gothenburg, Sweden, 22--24 May 2017. Linköping University Electronic Press, 211--215.Google Scholar
- Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 1724--1734.Google ScholarCross Ref
- Wei Di, Anurag Bhardwaj, Vignesh Jagadeesh, Robinson Piramuthu, and Elizabeth Churchill. 2014. When relevance is not enough: Promoting visual attractiveness for fashion e-commerce. arXiv preprint arXiv:1406.3561 (2014).Google Scholar
- Zhicheng Dou, Ruihua Song, Xiaojie Yuan, and Ji-Rong Wen. 2008. Are click-through data adequate for learning web search rankings?. In Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 73--82.Google ScholarDigital Library
- Huizhong Duan, ChengXiang Zhai, Jinxing Cheng, and Abhishek Gattani. 2013. Supporting keyword search in product database: a probabilistic approach. Proceedings of the VLDB Endowment, Vol. 6, 14 (2013), 1786--1797.Google ScholarDigital Library
- Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. 2010. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, Vol. 11, Feb (2010), 625--660.Google ScholarDigital Library
- Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 1126--1135.Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780.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. 2042--2050.Google Scholar
- 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 & Knowledge Management. ACM, 2333--2338.Google ScholarDigital Library
- Shubhra Kanti Karmaker Santu, Parikshit Sondhi, and ChengXiang Zhai. 2017. On application of learning to rank for e-commerce search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 475--484.Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. International Conference on Lerning Representations (ICLR) (2015).Google Scholar
- Taku Kudo and John Richardson. 2018. Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018).Google Scholar
- 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
- Maggie Yundi Li, Stanley Kok, and Liling Tan. 2018. Don't Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation. arXiv preprint arXiv:1812.05774 (2018).Google Scholar
- Bo Long, Jiang Bian, Anlei Dong, and Yi Chang. 2012. Enhancing product search by best-selling prediction in e-commerce. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2479--2482.Google ScholarDigital Library
- Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective Approaches to Attention-based Neural Machine Translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1412--1421.Google ScholarCross Ref
- Alessandro Magnani, Feng Liu, Min Xie, and Somnath Banerjee. 2019. Neural Product Retrieval at Walmart. com. In Companion Proceedings of The 2019 World Wide Web Conference. ACM, 367--372.Google ScholarDigital Library
- Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2016. Text matching as image recognition. In Thirtieth AAAI Conference on Artificial Intelligence.Google ScholarDigital Library
- Ankur Parikh, Oscar T"ackström, Dipanjan Das, and Jakob Uszkoreit. 2016. A Decomposable Attention Model for Natural Language Inference. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2249--2255.Google ScholarCross Ref
- 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
- Gerard Salton and Christopher Buckley. 1988. Term-weighting approaches in automatic text retrieval. Information processing & management, Vol. 24, 5 (1988), 513--523.Google Scholar
- Gerard Salton, Anita Wong, and Chung-Shu Yang. 1975. A vector space model for automatic indexing. Commun. ACM, Vol. 18, 11 (1975), 613--620.Google ScholarDigital Library
- Shota Sasaki, Shuo Sun, Shigehiko Schamoni, Kevin Duh, and Kentaro Inui. 2018. Cross-lingual learning-to-rank with shared representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 458--463.Google ScholarCross Ref
- 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
- Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems. 3104--3112.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008.Google Scholar
- Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, and Xueqi Cheng. 2016. A deep architecture for semantic matching with multiple positional sentence representations. In Thirtieth AAAI Conference on Artificial Intelligence.Google ScholarDigital Library
- Liang Wu, Diane Hu, Liangjie Hong, and Huan Liu. 2018. Turning clicks into purchases: Revenue optimization for product search in e-commerce. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 365--374.Google ScholarDigital Library
- Wenpeng Yin, Hinrich Schütze, Bing Xiang, and Bowen Zhou. 2016. Abcnn: Attention-based convolutional neural network for modeling sentence pairs. Transactions of the Association for Computational Linguistics, Vol. 4 (2016), 259--272.Google ScholarCross Ref
- Ming Zhu, Aman Ahuja, Wei Wei, and Chandan K Reddy. 2019. A Hierarchical Attention Retrieval Model for Healthcare Question Answering. In The World Wide Web Conference. ACM, 2472--2482.Google ScholarDigital Library
Index Terms
- Language-Agnostic Representation Learning for Product Search on E-Commerce Platforms
Recommendations
Enhancing product search by best-selling prediction in e-commerce
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementWith the rapid growth of E-Commerce on the Internet, online product search service has emerged as a popular and effective paradigm for customers to find desired products and select transactions. Most product search engines today are based on adaptations ...
Learning a Hierarchical Embedding Model for Personalized Product Search
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalProduct search is an important part of online shopping. In contrast to many search tasks, the objectives of product search are not confined to retrieving relevant products. Instead, it focuses on finding items that satisfy the needs of individuals and ...
A Zero Attention Model for Personalized Product Search
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge ManagementProduct search is one of the most popular methods for people to discover and purchase products on e-commerce websites. Because personal preferences often have an important influence on the purchase decision of each customer, it is intuitive that ...
Comments