Intent Contrastive Learning for Sequential Recommendation [PDF]
The Web Conference, 2022 Users’ interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.). However, users’ underlying intents are often unobserved/latent, making it challenging to leverage such latent intents ...
Yongjun Chen +4 more
semanticscholar +1 more source
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning [PDF]
International Conference on Software Engineering, 2023 Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities.
Mingyang Geng +7 more
semanticscholar +1 more source
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 Precisely understanding users' contextual search intent has been an important challenge for conversational search. As conversational search sessions are much more diverse and long-tailed, existing methods trained on limited data still show unsatisfactory
Kelong Mao +4 more
semanticscholar +1 more source
Efficient Intent Detection with Dual Sentence Encoders [PDF]
NLP4CONVAI, 2020 Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i.e., in few-shot setups).
I. Casanueva +4 more
semanticscholar +1 more source
How to Communicate Robot Motion Intent: A Scoping Review [PDF]
International Conference on Human Factors in Computing Systems, 2023 Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot’s motion intent to avoid task failures and foster ...
Max Pascher +3 more
semanticscholar +1 more source
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies [PDF]
ACM Transactions on the Web, 2023 Understanding user intents in information access scenarios can help us provide more relevant and personalized search results and recommendations. However, analyzing user intents is not easy, especially for emerging forms of Web search such as Artificial ...
C. Shah +15 more
semanticscholar +1 more source
Exploring Zero and Few-shot Techniques for Intent Classification [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers puts a constraint on storage space as well.
S. Parikh +3 more
semanticscholar +1 more source
Multi-view Intent Disentangle Graph Networks for Bundle Recommendation [PDF]
AAAI Conference on Artificial Intelligence, 2022 Bundle recommendation aims to recommend the user a bundle of items as a whole. Previous models capture user’s preferences on both items and the association of items. Nevertheless, they usually neglect the diversity of user’s intents on adopting items and
Sen Zhao +3 more
semanticscholar +1 more source
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction [PDF]
Conference on Empirical Methods in Natural Language Processing, 2019 Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example.
Stefan Larson +10 more
semanticscholar +1 more source
Intent-aware Recommendation via Disentangled Graph Contrastive Learning [PDF]
International Joint Conference on Artificial Intelligence, 2023 Graph neural network (GNN) based recommender systems have become one of the mainstream trends due to the powerful learning ability from user behavior data.
Yuling Wang +7 more
semanticscholar +1 more source