The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users’ personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system interaction paradigm, (iv) enhancing trustworthiness checks, and (v) refining evaluation methodologies for generative recommendations. With generative models advancing rapidly, an increasing body of research is emerging in these domains, underscoring the timeliness and critical importance of this workshop. The related research will introduce innovative technologies to recommender systems and contribute to fresh challenges in both academia and industry. In the long term, this research direction has the potential to revolutionize the traditional recommender paradigms and foster the development of next-generation recommender systems. [PDF]
Activity type | Time (Singapore Time) | Title |
---|---|---|
Opening | 9.00am | |
Keynote | 9.00am-9.35am | Keynote 1 by Dr. Minmin Chen from Google |
Paper oral | 9.35am-9.45am | Multimodal Conditioned Diffusion Model for Recommendation |
Paper oral | 9.45pm-9.55pm | Diffusion Recommendation with Implicit Sequence Influence |
Keynote | 9.55am-10.30am | Keynote 2 by Dr. Jiaqi Zhai and Dr. Rui Li from Meta |
Coffee break | 10.30am-11.00am | |
Keynote | 11.00am-11.35am | Keynote 3 by Prof. Julian McAuley from UCSD |
Paper oral | 11.35am-11.45am | OutfitGPT: LLMs as Fashion Outfit Generator and Recommender |
Paper oral | 11.45am-11.55am | Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation |
Paper oral | 11.55am-12.05pm | A Study of Implicit User Unfairness in Large Language Models for Recommendation |
Paper oral | 12.05pm-12.15pm | How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation |
Paper oral | 12.15pm-12.25pm | Controllable and Transparent Textual Latents for Recommender Systems |
Paper oral | 12.25pm-12.35pm | Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning |
The main objective of this workshop is to encourage pioneering research in the integration of generative models with recommender systems, with a specific focus on five key aspects. First, this workshop will motivate active researchers to utilize generative models for enhancing recommender algorithms and refining user modeling. Second, it promotes utilizing generative models to generate diverse content, i.e., AI-generated content (AIGC), in certain situations, complementing human-generated content to satisfy a broader range of user preferences and information needs. Third, it embraces substantial innovations in user interactions with recommender systems, possibly driven by the boom of large language models (LLMs). Fourth, the workshop will highlight the significance of trust in employing generative models for recommendations, encompassing aspects like content trustworthiness, algorithmic biases, and adherence to evolving ethical and legal standards. Lastly, the workshop will prompt researchers to develop diverse methods for the evaluation, including novel metrics and human evaluation approaches.
The workshop provides an invaluable forum for researchers to present the latest advancements in the rapidly evolving field of recommender systems. We welcome original submissions focusing on generative models in recommender systems, including a range of relevant topics:
Submitted papers must be a single PDF file in the template of ACM WWW 2024. Submissions can be of varying length from 4 to 8 pages, plus unlimited pages for references. The authors may decide on the appropriate length of the paper as no distinction is made between long and short papers. All submitted papers will follow the "double-blind" review policy and undergo the same review process and duration. Expert peer reviewers in the field will assess all papers based on their relevance to the workshop, scientific novelty, and technical quality.
Submission site: https://easychair.org/conferences/?conf=thewebconf2024_workshops (track of "The 2nd Workshop on Recommendation with Generative Models"). Accepted papers have the option to be included in the WWW Companions proceedings. And there will be the Best Paper Award and the Best Paper Runner-Up Award for this workshop!
National University of Singapore
University of Science and Technology of China
National University of Singapore
University of Science and Technology of China
Huawei Noah's Ark Lab, China
Huawei Noah’s Ark Lab, Singapore
City University of Hong Kong
Renmin University of China
Kuaishou Technology, Beijing, China
University of Science and Technology of China