Editable User Profiles for Controllable Text Recommendation

Autor:	Mysore, Sheshera, Jasim, Mahmood, McCallum, Andrew, Zamani, Hamed
Rok vydání:	2023
Předmět:	Computer Science - Information Retrieval Computer Science - Computation and Language Computer Science - Human-Computer Interaction Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this problem by proposing LACE, a novel concept value bottleneck model for controllable text recommendations. LACE represents each user with a succinct set of human-readable concepts through retrieval given user-interacted documents and learns personalized representations of the concepts based on user documents. This concept based user profile is then leveraged to make recommendations. The design of our model affords control over the recommendations through a number of intuitive interactions with a transparent user profile. We first establish the quality of recommendations obtained from LACE in an offline evaluation on three recommendation tasks spanning six datasets in warm-start, cold-start, and zero-shot setups. Next, we validate the controllability of LACE under simulated user interactions. Finally, we implement LACE in an interactive controllable recommender system and conduct a user study to demonstrate that users are able to improve the quality of recommendations they receive through interactions with an editable user profile. Comment: SIGIR-2023 paper with extended results
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.04250 Zobrazit plný text záznamu View this record from Arxiv