Diffusion Model as a Base for Cold Item Recommendation
Source abstract
Abstract
Cold items are a critical problem in the recommendation domain because newly introduced items lack user–item interactions to train accurate collaborative filters (CFs). Recent studies have adopted neural networks such as MLPs and autoencoders to predict collaborative embeddings learned by CFs, using items’ side information available at the time of registration. As a generative model, diffusion models have achieved success in various fields, such as image generation and natural language processing, through their superior generative capability. This paper proposes a diffusion model as a base for cold item recommendation by generating collaborative embeddings for cold items. First, using a diffusion model with our customized predictor, we directly generate items’ collaborative embeddings conditioned on their side information. Then, a second-stage refiner, which adopts simple MLPs and dropout, is trained to calculate the final recommendation scores. The proposed method requires only the user embeddings from the existing model and the side information of cold items, making it easy to integrate into existing recommender systems. Extensive experiments on real-world datasets show that the proposed method outperforms state-of-the-art baseline methods and indicates the potential of diffusion models in cold item recommendation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Dong-A University, Busan 49315, Republic of Korea
Funding
Dong-A University research fund
References (70)
Show source-provided references
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