LUMI-RecSys @ ACM RecSys 2024

Explore

LUMI-RecSys @ ACM RecSys 2024

The 1st workshop on Large Language Models for User Modeling and Interaction in Recommendation Systems

Overview

Traditional item recommendation systems ingest user historical behaviours and predict items of interest to their users. Recommendations can then be fed back to the users. The latest generation of large language models (LLM) can generate high-quality text, can follow detailed instructions, and can access a wide amount of information (encoded in their weights and through retrieval mechanisms). These characteristics enable both new ways to learn user information (i.e. through user-generated instructions or prompts) and new ways of modeling user representations (i.e text-based user representations or personalized recommendation justifications).

Topics of Interest

We welcome papers on all topics related to LLMs and explanable user modeling. These topics include but not limited to:

Transparent and Scrutable User Representation: LLMs can generate textual summaries of user profiles that capture their preferences over time. These summaries provide users with a transparent view of how the system perceives their preferences, facilitating a better understanding of the recommendation process. These scrutable user representations can be edited by users to control the recommendation.

Natural Language Interaction for Preference Elicitation: Beyond static user profiles, LLMs can engage in natural language dialogue to elicit user preferences in a conversational manner. This allows for a more nuanced understanding of user preferences, including context, mood, subjective features, and specific needs at the moment, which can be difficult to infer from behavior alone. This also includes the development of conversational policies powered by reinforcement learning.

Explanation-driven Feedback Loop: By providing explanations for recommendations, LLM-based systems enable a feedback loop where users can directly interact with the explanations (e.g., by expressing agreement or disagreement). This feedback can be used to refine user models in real-time, enhancing the accuracy of recommendations and the user's trust in the system.

Personalized Recommendation Explanation: LLMs can be used to provide recommendation explanations to users rooted in content and collaborative signals. Recognizing that different users have different preferences for how explanations are presented, LLMs can also tailor the style, complexity, and detail level of explanations to satisfy diverse user needs, enhancing the personalization and effectiveness of the recommender system.

Ethical and Bias Considerations: With the use of LLMs in explainable user modeling, there is an opportunity to address ethical considerations and biases in recommendations. LLMs can help identify and explain potential biases in the user model or the recommendation process, fostering a more ethical and fair approach to personalized recommendations.

Call for Papers

All the accepted submissions will be presented at the workshop, either in oral sessions or the poster session.

We inivite quality research contributions and application studies in different formats:

Original research papers, both long (limited to 8 content pages) and short (limited to 4 content pages);

Extended abstracts for vision, perspective, and research proposal (4 content pages);

Posters or demos on user modeling and interaction in recommendation systems through LLMs (4 content pages).

Workshop papers that have been previously published or are under review for another journal, conference or workshop should not be considered for publication. Workshop papers should not exceed 12 pages in length (maximum 8 pages for the main paper content + maximum 2 pages for appendixes + maximum 2 pages for references). Papers must be submitted in PDF format according to the ACM template published in the ACM guidelines, selecting the generic “sigconf” sample. The PDF files must have all non-standard fonts embedded. Workshop papers must be self-contained and in English. The reviewing process is double-blinded.

At least one author of each accepted workshop paper has to register for the main conference. Workshop attendance is only granted for registered participants.

Important Dates

Time zone:

Anywhere on Earth (AoE)⁠

⁠

Submission deadline

Acceptance notification

Camera Ready

Aug. 5, 2024

Aug. 26, 2024

Sep. 9, 2024

Organizers

⁠

Laurent Charlin HEC Montréal, Mila-Québec AI Institute, CIFAR AI Chair

Laurent Charlin     HEC Montréal, Mila-Québec AI Institute, CIFAR AI Chair

⁠

Montréal, Canada

⁠

Rahul Jha Senior Research Scientist at Netflix

Rahul Jha       Senior Research Scientist at Netflix

⁠

Los Gatos, US

⁠

Haolun Wu McGill University Mila-Québec AI Institute

Haolun Wu         McGill University Mila-Québec AI Institute

⁠

Montréal, Canada

⁠

Emiliano Penaloza Unversité de Montréal, Mila-Québec AI Institute

Emiliano Penaloza Unversité de Montréal, Mila-Québec AI Institute

⁠

Montréal, Canada

⁠

Cem Subakan Université Laval, Mila-Québec AI Institute

Cem Subakan Université Laval, Mila-Québec AI Institute

⁠

Montréal, Canada

Want to print your doc?
This is not the way.

Try clicking the ⋯ next to your doc name or using a keyboard shortcut (

CtrlP

) instead.