Bin Wu
ABSTRACT
Personalized product search that provides users with customized search services is an important task for e-commerce platforms. This task remains a challenge when inferring users’ preferences from few records or even no records, which is also known as the few-shot or zero-shot learning problem. In this paper, we propose a Bayesian Online Meta-Learning Model (BOML), which transfers meta-knowledge, from the inference for other users’ preferences, to help to infer the current user’s interest behind her/his few or even no historical records. To extract meta-knowledge from various inference patterns, our model constructs a mixture of meta-knowledge and transfers the corresponding meta-knowledge to the specific user according to her/his records. Based on the meta-knowledge learned from other similar inferences, our proposed model searches a ranked list of products to meet users’ personalized query intents for those with few search records (i.e., few-shot learning problem) or even no search records (i.e., zero-shot learning problem). Under the records arriving sequentially setting, we propose an online variational inference algorithm to update meta-knowledge over time. Experimental results demonstrate that our proposed BOML outperforms state-of-the-art algorithms.
- Qingyao Ai, Daniel N Hill, SVN Vishwanathan, and W Bruce Croft. 2019. A zero attention model for personalized product search. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 379–388.Google Scholar
Digital Library
- 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. 645–654.Google ScholarDigital Library
- Terry Anderson. 2008. The theory and practice of online learning. Athabasca University Press.Google Scholar
- Yutian Chen, Matthew W Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Timothy P Lillicrap, Matt Botvinick, and Nando Freitas. 2017. Learning to learn without gradient descent by gradient descent. In International Conference on Machine Learning. PMLR, 748–756.Google Scholar
- W Bruce Croft, Donald Metzler, and Trevor Strohman. 2010. Search engines: Information retrieval in practice. Vol. 520. Addison-Wesley Reading.Google Scholar
- Tristan Deleu and Yoshua Bengio. 2018. The effects of negative adaptation in model-agnostic meta-learning. arXiv preprint arXiv:1812.02159(2018).Google Scholar
- Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, and Massimiliano Pontil. 2019. Online-within-online meta-learning. In ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), Vol. 32. Neural Information Processing Systems (NeurIPS 2019), 1–11.Google Scholar
- Huizhong Duan and ChengXiang Zhai. 2015. Mining coordinated intent representation for entity search and recommendation. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 333–342.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 6, 14 (2013), 1786–1797.Google ScholarDigital Library
- Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning. PMLR, 1126–1135.Google Scholar
- Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine. 2019. Online meta-learning. In International Conference on Machine Learning. PMLR, 1920–1930.Google Scholar
- Chelsea Finn, Kelvin Xu, and Sergey Levine. 2018. Probabilistic model-agnostic meta-learning. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 9537–9548.Google Scholar
- Songwei Ge, Zhicheng Dou, Zhengbao Jiang, Jian-Yun Nie, and Ji-Rong Wen. 2018. Personalizing search results using hierarchical RNN with query-aware attention. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 347–356.Google ScholarDigital Library
- Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner. 2018. Meta-Learning Probabilistic Inference for Prediction. In International Conference on Learning Representations.Google Scholar
- Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Yinglong Wang, Jun Ma, and Mohan Kankanhalli. 2019. Attentive long short-term preference modeling for personalized product search. ACM Transactions on Information Systems (TOIS) 37, 2 (2019), 1–27.Google ScholarDigital Library
- David Ha, Andrew Dai, and Quoc V Le. 2016. Hypernetworks. arXiv preprint arXiv:1609.09106(2016).Google Scholar
- Malay Haldar, Prashant Ramanathan, Tyler Sax, Mustafa Abdool, Lanbo Zhang, Aamir Mansawala, Shulin Yang, Bradley Turnbull, and Junshuo Liao. 2020. Improving deep learning for airbnb search. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2822–2830.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. 507–517.Google ScholarDigital Library
- Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. 2020. Meta-learning in neural networks: A survey. arXiv preprint arXiv:2004.05439(2020).Google Scholar
- Ekaterina Iakovleva, Jakob Verbeek, and Karteek Alahari. 2020. Meta-Learning with Shared Amortized Variational Inference. In International Conference on Machine Learning. PMLR, 4572–4582.Google Scholar
- Ghassen Jerfel, Erin Grant, Thomas L Griffiths, and Katherine Heller. 2019. Reconciling meta-learning and continual learning with online mixtures of tasks. Advances in Neural Information Processing Systems 32 (2019).Google Scholar
- Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114(2013).Google Scholar
- Shangsong Liang. 2019. Unsupervised semantic generative adversarial networks for expert retrieval. In The World Wide Web Conference. 1039–1050.Google ScholarDigital Library
- Zaiqiao Meng, Shangsong Liang, Hongyan Bao, and Xiangliang Zhang. 2019. Co-embedding attributed networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. 393–401.Google ScholarDigital Library
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546(2013).Google Scholar
- Tsendsuren Munkhdalai and Hong Yu. 2017. Meta networks. In International Conference on Machine Learning. PMLR, 2554–2563.Google Scholar
- Radford M Neal and Geoffrey E Hinton. 1998. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in graphical models. Springer, 355–368.Google Scholar
- Alex Nichol, Joshua Achiam, and John Schulman. 2018. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999(2018).Google Scholar
- Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste. 2018. Tadam: Task dependent adaptive metric for improved few-shot learning. Advances in neural information processing systems 31 (2018).Google Scholar
- Yaoxin Pan, Shangsong Liang, Jiaxin Ren, Zaiqiao Meng, and Qiang Zhang. 2021. Personalized, Sequential, Attentive, Metric-Aware Product Search. ACM Transactions on Information Systems (TOIS) 40, 2 (2021), 1–29.Google ScholarDigital Library
- Sachin Ravi and Alex Beatson. 2019. Amortized Bayesian Meta-Learning.. In ICLR (Poster).Google Scholar
- Sachin Ravi and Hugo Larochelle. 2016. Optimization as a model for few-shot learning. (2016).Google Scholar
- Douglas A Reynolds. 2009. Gaussian Mixture Models.Encyclopedia of biometrics 741 (2009), 659–663.Google Scholar
- Michael T Rosenstein, Zvika Marx, Leslie Pack Kaelbling, and Thomas G Dietterich. 2005. To transfer or not to transfer. In NIPS 2005 workshop on transfer learning, Vol. 898. 1–4.Google Scholar
- Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 4080–4090.Google Scholar
- Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE.Journal of machine learning research 9, 11 (2008).Google Scholar
- Christophe Van Gysel, Maarten de Rijke, and Evangelos Kanoulas. 2016. Learning latent vector spaces for product search. In Proceedings of the 25th ACM international on conference on information and knowledge management. 165–174.Google ScholarDigital Library
- Vinay Kumar Verma, Dhanajit Brahma, and Piyush Rai. 2020. Meta-learning for generalized zero-shot learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 6062–6069.Google ScholarCross Ref
- Teng Xiao, Jiaxin Ren, Zaiqiao Meng, Huan Sun, and Shangsong Liang. 2019. Dynamic bayesian metric learning for personalized product search. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1693–1702.Google ScholarDigital Library
- Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Li, Richard Socher, and Caiming Xiong. 2020. Online Structured Meta-learning. arXiv preprint arXiv:2010.11545(2020).Google Scholar
- Pauching Yap, Hippolyt Ritter, and David Barber. 2021. Addressing Catastrophic Forgetting in Few-Shot Problems. In International Conference on Machine Learning. PMLR, 11909–11919.Google Scholar
- Qiang Zhang, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang, and Emine Yilmaz. 2021. Variational Continual Bayesian Meta-Learning. Advances in Neural Information Processing Systems 34 (2021).Google Scholar
- Fengwei Zhou, Bin Wu, and Zhenguo Li. 2018. Deep meta-learning: Learning to learn in the concept space. arXiv preprint arXiv:1802.03596(2018).Google Scholar
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- Meta-Learning Helps Personalized Product Search
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