research-article

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Seeker: Real-Time Interactive Search

Authors:

Michael Vogelsong,

Benjamin Snyder,

Houssam NassifAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2867 - 2875

https://doi.org/10.1145/3292500.3330733

Published: 25 July 2019 Publication History

Abstract

This paper introduces Seeker, a system that allows users to adaptively refine search rankings in real time, through a series of feedbacks in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding space, allowing the user to query using a representation of the item (like a tune for a song, or a picture for an object). However, this approach requires the user to possess an example representation of their desired item. Additionally, most current search systems do not allow the user to dynamically adapt the results with further feedback. On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: "Is this item similar to what you have in mind?" With this assumption, our algorithm allows for users to provide sequential feedback on search results to adapt the search feed. We show that our proposed approach works well both qualitatively and quantitatively. Unlike most previous representation-based search systems, we can quantify the quality of our algorithm by evaluating humans-in-the-loop experiments.

References

[1]

Emile Aarts and Jan Korst. 1988. Simulated annealing and Boltzmann machines. (1988).

Digital Library

[2]

Raja Hafiz Affandi, Alex Kulesza, and Emily B. Fox. 2012. Markov Determinantal Point Processes. In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI).

Digital Library

[3]

Artem Babenko, Anton Slesarev, Alexander Chigorin, and Victor S. Lempitsky. 2014. Neural Codes for Image Retrieval. In The European Conference on Computer Vision (ECCV). Zurich, Switzerland, 584--599.

[4]

Sébastien Bubeck, Nicolo Cesa-Bianchi, et almbox. 2012. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends® in Machine Learning, Vol. 5, 1 (2012), 1--122.

[5]

Nicolò Cesa-Bianchi, Claudio Gentile, Gábor Lugosi, and Gergely Neu. 2017. Boltzmann exploration done right. In Advances in Neural Information Processing Systems. 6284--6293.

Digital Library

[6]

Olivier Chapelle and Lihong Li. 2011. An empirical evaluation of thompson sampling. In Advances in neural information processing systems. 2249--2257.

Digital Library

[7]

Lin Chen, Andreas Krause, and Amin Karbasi. 2017. Interactive Submodular Bandit. In Advances in Neural Information Processing Systems 30 (NIPS). 141--152.

Digital Library

[8]

Ritendra Datta, Dhiraj Joshi, Jia Li, and James Z Wang. 2008. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (Csur), Vol. 40, 2 (2008), 5.

Digital Library

[9]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[10]

M Hadi Kiapour, Xufeng Han, Svetlana Lazebnik, Alexander C Berg, and Tamara L Berg. 2015. Where to buy it: Matching street clothing photos in online shops. In Proceedings of the IEEE international conference on computer vision. 3343--3351.

Digital Library

[11]

Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. Evaluating Collaborative Filtering Recommender Systems. ACM Trans. Inf. Syst., Vol. 22, 1 (2004), 5--53.

Digital Library

[12]

Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, and S.V.N. Vishwanathan. 2017. An Efficient Bandit Algorithm for Realtime Multivariate Optimization. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17). ACM, 1813--1821.

Digital Library

[13]

Houdong Hu, Yan Wang, Linjun Yang, Pavel Komlev, and Li Huang. 2018. Web-Scale Responsive Visual Search at Bing. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '18). ACM.

Digital Library

[14]

Hsin-Yuan Huang, Eunsol Choi, and Wen-tau Yih. 2018. Flowqa: Grasping flow in history for conversational machine comprehension. arXiv preprint arXiv:1810.06683 (2018).

[15]

Lalit Jain, Kevin G Jamieson, and Rob Nowak. 2016. Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. In Advances in Neural Information Processing Systems 29 (NIPS). Barcelona, Spain, 2711--2719.

Digital Library

[16]

Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In Fifth International Conference on Learning Representations (ICLR) .

[17]

Yushi Jing, David Liu, Dmitry Kislyuk, Andrew Zhai, Jiajing Xu, Jeff Donahue, and Sarah Tavel. 2015a. Visual search at pinterest. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1889--1898.

Digital Library

[18]

Yushi Jing, David Liu, Dmitry Kislyuk, Andrew Zhai, Jiajing Xu, Jeff Donahue, and Sarah Tavel. 2015b. Visual Search at Pinterest. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15). ACM.

Digital Library

[19]

Hanjiang Lai, Yan Pan, Ye Liu, and Shuicheng Yan. 2015. Simultaneous Feature Learning and Hash Coding with Deep Neural Networks. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, 3270--3278.

[20]

Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In International conference on machine learning. 1188--1196.

Digital Library

[21]

Jerri L. Ledford. 2015. Search engine optimization bible second ed.). Wiley, Indianapolis, IN.

Digital Library

[22]

Wu-Jun Li, Sheng Wang, and Wang-Cheng Kang. 2016. Feature Learning Based Deep Supervised Hashing with Pairwise Labels. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). 1711--1717.

Digital Library

[23]

Chris J Maddison, Daniel Tarlow, and Tom Minka. 2014. A* Sampling. In Advances in Neural Information Processing Systems 27 (NIPS) .

Digital Library

[24]

Houssam Nassif, Kemal Oral Cansizlar, Mitchell Goodman, and S. V. N. Vishwanathan. 2016. Diversifying Music Recommendations. In Proceedings of Machine Learning for Music Discovery Workshop at $33^rd$ International Conference on Machine Learning (ICML) .

[25]

Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018).

[26]

Daniel J Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen, et almbox. 2018. A tutorial on thompson sampling. Foundations and Trends® in Machine Learning, Vol. 11, 1 (2018), 1--96.

Digital Library

[27]

Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition . 815--823.

[28]

Pararth Shah, Dilek Hakkani-Tür, Gokhan Tür, Abhinav Rastogi, Ankur Bapna, Neha Nayak, and Larry Heck. 2018. Building a conversational agent overnight with dialogue self-play. arXiv preprint arXiv:1801.04871 (2018).

[29]

B. Smith and G. Linden. 2017. Two Decades of Recommender Systems at Amazon.com. IEEE Internet Computing, Vol. 21, 3 (2017), 12--18.

Digital Library

[30]

Neil Stewart, Gordon DA Brown, and Nick Chater. 2005. Absolute identification by relative judgment. Psychological review, Vol. 112, 4 (2005), 881.

[31]

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. 2016. Rethinking the Inception Architecture for Computer Vision. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, 2818--2826.

[32]

Csaba Szepesvári. 2010. Algorithms for Reinforcement Learning .Morgan & Claypool Publishers.

[33]

Omer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, and Adam Tauman Kalai. 2011. Adaptively learning the crowd kernel. arXiv preprint arXiv:1105.1033 (2011).

Digital Library

[34]

Choon Hui Teo, Houssam Nassif, Daniel Hill, Sriram Srinivasan, Mitchell Goodman, Vijai Mohan, and S.V.N. Vishwanathan. 2016. Adaptive, Personalized Diversity for Visual Discovery. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys). ACM, Boston, MA, 35--38.

Digital Library

[35]

William R Thompson. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika (1933), 285--294.

[36]

Joannès Vermorel and Mehryar Mohri. 2005. Multi-armed Bandit Algorithms and Empirical Evaluation. In Proceedings of the 16th European Conference on Machine Learning (ECML'05). 437--448.

Digital Library

[37]

Tsung-Hsien Wen, David Vandyke, Nikola Mrksic, Milica Gasic, Lina M Rojas-Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young. 2016. A network-based end-to-end trainable task-oriented dialogue system. arXiv preprint arXiv:1604.04562 (2016).

[38]

Rongkai Xia, Yan Pan, Hanjiang Lai1, Cong Liu, and Shuicheng Yan. 2014. Supervised Hashing for Image Retrieval via Image Representation Learning. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI-14). 2156--2162.

Digital Library

[39]

Fan Yang, Ajinkya Kale, Yury Bubnov, Leon Stein, Qiaosong Wang, Hadi Kiapour, and Robinson Piramuthu. 2017. Visual Search at eBay. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17). ACM.

Digital Library

[40]

Yanhao Zhang, Pan Pan, Yun Zheng, Kang Zhao, Yingya Zhang, Xiaofeng Ren, and Rong Jin. 2018. Visual Search at Alibaba. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '18). ACM.

Digital Library

[41]

Han Zhu, Mingsheng Long, Jianmin Wang, and Yue Cao. 2016. Deep Hashing Network for Efficient Similarity Retrieval. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). 2415--2421.

Digital Library

Cited By

Geng SNassif HManzanares CEvans RShpitser I(2023)A data-driven state aggregation approach for dynamic discrete choice modelsProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3625895(647-657)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3625895
Kong FLi YNassif HFiez THenao RChakrabarti SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Neural Insights for Digital Marketing Content DesignProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599875(4320-4332)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599875
Huijben IKool WPaulus Mvan Sloun R(2023)A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.315704245:2(1353-1371)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TPAMI.2022.3157042
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Published In

cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 July 2019

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KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Geng SNassif HManzanares CEvans RShpitser I(2023)A data-driven state aggregation approach for dynamic discrete choice modelsProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3625895(647-657)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3625895
Kong FLi YNassif HFiez THenao RChakrabarti SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Neural Insights for Digital Marketing Content DesignProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599875(4320-4332)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599875
Huijben IKool WPaulus Mvan Sloun R(2023)A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.315704245:2(1353-1371)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TPAMI.2022.3157042
Kim Y(2023)Deep Stochastic Logic Gate NetworksIEEE Access10.1109/ACCESS.2023.332862211(122488-122501)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3328622
Geng SNassif HManzanares CReppen ASircar RDaumé HSingh A(2020)Deep PQRProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525259(3431-3441)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525259

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