Deep Sequential Models for Task Satisfaction Prediction

Author:

Mehrotra Rishabh1,Awadallah Ahmed Hassan2,Shokouhi Milad2,Yilmaz Emine1,Zitouni Imed2,El Kholy Ahmed3,Khabsa Madian2

Affiliation:

1. University College London, London, United Kingdom

2. Microsoft Inc., Redmond, WA, USA

3. Microsoft Inc, Redmond, USA

Publisher

ACM

Cited by 22 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Intent-Satisfaction Modeling: From Music to Video Streaming;ACM Transactions on Recommender Systems;2023-08-07

2. Practice and Challenges in Building a Business-oriented Search Engine Quality Metric;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18

3. RACL: A robust adaptive contrastive learning method for conversational satisfaction prediction;Pattern Recognition;2023-06

4. A Data-Driven Analysis of Behaviors in Data Curation Processes;ACM Transactions on Information Systems;2023-02-07

5. Behavior Sequence Transformer Applied on SERP Evaluation and Model Interpretation;2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA);2022-12

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