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Intent Preference Decoupling for User Representation on Online Recommender System

Zhaoyang Liu, Haokun Chen, Fei Sun, Xu Xie, Jinyang Gao, Bolin Ding, Yanyan Shen
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
Accurately characterizing the user's current interest is the core of recommender systems. However, users' interests are dynamic and affected by intent factors and preference factors.  ...  In this paper, we propose a novel learning strategy named FLIP to decouple the learning of intent and preference under the online settings.  ...  Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  ... 
doi:10.24963/ijcai.2020/353 dblp:conf/ijcai/WangL20a fatcat:3zig2w4pc5bd7iinrcxfaxjaiq

A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery

Yijing Luo, Bo Han, Chen Gong
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
Based on the cluster membership, we utilize the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively.  ...  To address this issue, this paper introduces a bi-level learning paradigm termed "Spectral Cluster Discovery" (SCD) for combating with noisy labels.  ...  Thanks for the hard work of the co-authors who are devoted in this work. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  ... 
doi:10.24963/ijcai.2020/357 dblp:conf/ijcai/LiuCSXGDS20 fatcat:46whotughnd6djqyim2hie6pna

Price Mechanism for Knowledge Transfer: An Integrative Theory

Ming-Hui Huang, Eric T.G. Wang, Abraham Seidmann
2007 Journal of Management Information Systems  
Transaction Decoupling for Knowledge Preferences The transaction decoupling perspective’ |25, 74], on the other hand, provides an explanation for the irrationality of “preferring repository pricing but  ...  Knowledge Preferences Knowledge preferences were assessed in terms of knowledge usefulness, user satisfac- tion, willingness to recommend to others, and intention to repurchase.  ... 
doi:10.2753/mis0742-1222240303 fatcat:g3rjrqajlrd57cqw354a4a7hba

Semantic audio content-based music recommendation and visualization based on user preference examples

Dmitry Bogdanov, Martín Haro, Ferdinand Fuhrmann, Anna Xambó, Emilia Gómez, Perfecto Herrera
2013 Information Processing & Management  
Semantic audio content-based music recommendation and visualization based on user preference examples.  ...  In the present work, we focus on music recommender systems and consider explicit strategies to infer musical preferences of a user directly from the music audio data.  ...  Acknowledgements The authors thank all participants involved in the evaluation and Justin Salamon for proofreading.  ... 
doi:10.1016/j.ipm.2012.06.004 fatcat:y6bcjarzandwljl5qxqruv5yh4

Advances and Challenges in Conversational Recommender Systems: A Survey [article]

Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua
2021 arXiv   pre-print
Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications.  ...  In a CRS, users and the system can dynamically communicate through natural language interactions, which provide unprecedented opportunities to explicitly obtain the exact preference of users.  ...  Some studies propose interactive recommender systems [57, 177, 20, 226] , which mainly focus on improving the recommendation strategy online by leveraging real-time user feedback on previous recommended  ... 
arXiv:2101.09459v6 fatcat:j7djzhrv6fazpogmnj7r4e4f2y

VoCoG: An Intelligent, Non-Intrusive Assistant for Voice-based Collaborative Group-Viewing [article]

Sumit Shekhar, Aditya Siddhant, Anindya Shankar Bhandari, Nishant Yadav
2018 arXiv   pre-print
VoCoG incorporates an online recommendation algorithm, efficient methods for analyzing natural conversation and a graph-based method to fuse preferences of multiple users.  ...  It takes user conversation as input, making it non-intrusive. A usability survey of the system indicates that the system provides a good experience to the users as well as relevant recommendations.  ...  The system was then measured on different parameters following the methods for user-centric recommender system evaluation [46] , [47] . A.  ... 
arXiv:1811.07547v1 fatcat:2625b4pxeraefes55iy2hskmvy

Advances and challenges in conversational recommender systems: A survey

Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua
2021 AI Open  
A B S T R A C T Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications.  ...  In a CRS, users and the system can dynamically communicate through natural language interactions, which provide unprecedented opportunities to explicitly obtain the exact preference of users.  ...  the recommendation strategy online by leveraging real-time user feedback on previously recommended items.  ... 
doi:10.1016/j.aiopen.2021.06.002 fatcat:4r26fmsuvjcyla5wycb2ax62ha

CRSAL

Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Nguyen Quoc Viet Hung, Zi Huang, Xiangliang Zhang
2020 ACM Transactions on Information Systems (TOIS; Formerly: ACM Transactions on Office Information Systems)  
way for end-users to express their intentions and demands.  ...  However, existing conversational recommender systems only allow the systems to ask users for more preference information, while users' further questions and concerns about the recommended items (e.g.,  ...  ACKNOWLEDGMENTS We would like to thank our anonymous reviewers for providing insightful review comments and suggestions.  ... 
doi:10.1145/3394592 fatcat:vwl44xoo6jdm5nlw3bhh7qyzga

Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations [article]

Jianxun Lian, Iyad Batal, Zheng Liu, Akshay Soni, Eun Yong Kang, Yajun Wang, Xing Xie
2021 arXiv   pre-print
Precise user modeling is critical for online personalized recommendation services.  ...  SUM can be maintained and updated incrementally, making it feasible to be deployed for large-scale online serving. We conduct extensive experiments on two datasets.  ...  Different from general recommender systems [11, 12] that aim to learn users' long-term preference, sequential recommender systems take the sequence of user behaviors as context and predict her short-term  ... 
arXiv:2102.09211v3 fatcat:ko5u6yavh5h2ni4isnanhnieue

Understanding the customer value of co-designing individualised products

Hagen Habicht, Stefan R. Thallmaier
2017 International Journal of Technology Management  
Product perception fully mediates the relationship between co-design value and the intention to purchase as well as the intention to recommend the MC offer to others.  ...  Both have significant impact on product perception.  ...  We are very grateful for the exceptional trust and openness shown by Claudia Kieserling, who granted us direct access to Selve's online customer interface.  ... 
doi:10.1504/ijtm.2017.082359 fatcat:5ssufnawzvaqfeon53js4mlhvu

Understanding the customer value of co-designing individualised products

Hagen Habicht, Stefan R. Thallmaier
2017 International Journal of Technology Management  
Product perception fully mediates the relationship between co-design value and the intention to purchase as well as the intention to recommend the MC offer to others.  ...  Both have significant impact on product perception.  ...  We are very grateful for the exceptional trust and openness shown by Claudia Kieserling, who granted us direct access to Selve's online customer interface.  ... 
doi:10.1504/ijtm.2017.10003243 fatcat:2jzwt7zzlvdl3ae3exhjfdzg6m

Contextual Bandit Applications in Customer Support Bot [article]

Sandra Sajeev, Jade Huang, Nikos Karampatziakis, Matthew Hall, Sebastian Kochman, Weizhu Chen
2021 arXiv   pre-print
While our current use cases focus on intent disambiguation and contextual recommendation for support bots, we believe our methods can be extended to other domains.  ...  It includes intent disambiguation based on neural-linear bandits (NLB) and contextual recommendations based on a collection of multi-armed bandits (MAB).  ...  focus on intent disambiguation and contextual recommendation We drew inspiration from advances in recommendation systems for  ... 
arXiv:2112.03210v1 fatcat:m4eig5htorge7oko3xgkfs3ffe

Review and Analysis of Machine Learning and Soft Computing Approaches for User Modeling

Madhuri Potey, Pradeep K Sinha
2015 International journal of Web & Semantic Technology  
For the best results of user modelling, one should choose an appropriate way to do it i.e. by selecting the best suitable approach for the desired domain.  ...  The adequacy of user models depends mainly on the accuracy and precision of information that is retrieved to the user.  ...  ACKNOWLEDGEMENTS The authors are deeply indebted to all the researchers in the field and anonymous reviewers for their useful comments.  ... 
doi:10.5121/ijwest.2015.6104 fatcat:q43eezsnr5epredqr7xuhid3i4

A semantic web architecture for advocate agents to determine preferences and facilitate decision making

Wolfgang Ketter, Arun Batchu, Gary Berosik, Dan McCreary
2008 Proceedings of the 10th international conference on Electronic commerce - ICEC '08  
In the near future we expect the availability of a critical mass of data and metadata for use by intelligent agents that act on behalf of human users.  ...  These agents would identify, propose and capture new opportunities to assist human users in satisfying their goals, by traversing and acting on this semantically rich and abundant information.  ...  That is, recommendations for a specific user are based on the preferences of many different users [7, 8] (such as Movielens.umn.edu and Amazon.com) rather than tailored to the needs of an individual  ... 
doi:10.1145/1409540.1409554 dblp:conf/ACMicec/KetterBBM08 fatcat:iknyu3pxjvcz5lrfcrjyiz7uda

Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction [article]

Pi Qi, Xiaoqiang Zhu, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Kun Gai
2020 arXiv   pre-print
Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online advertising.  ...  Apart from the learning algorithm, we also introduce our hands-on experience on how to implement SIM in large scale industrial systems.  ...  Due to the rapid growth of user historical behavior * data, user interest modeling, which focuses on learning the intent representation of user interest, has been widely introduced in the CTR prediction  ... 
arXiv:2006.05639v2 fatcat:2fmsavma7bbhjpzked22q3mwy4
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