# Diffusion Model as a Base for Cold Item Recommendation
## 1 Introduction  
Collaborative filtering (CF) [1], a widely used approach for personalized recommendation, suffers from the cold item problem when newly introduced items are added to recommender systems [2, 3, 4]. \"Cold items\" refer to items that lack sufficient user interactions. Because CF methods rely on user-item interaction data to make accurate recommendations, their performance significantly degrades when recommending cold items. Extensive studies have been conducted on the cold item problem, as it is essential for ensuring users can discover relevant and diverse content in a timely manner, while also helping item providers gain early exposure for newly introduced items. One group of studies [5, 6, 7, 8, 9, 10, 11, 12] studied the methods for fast warm-up, which achieves accurate recommendations using as little interaction data as possible. Another group [13, 14, 15, 16, 17, 18, 19, 20, 21, 22] is interested in improving start-time recommendation accuracy when no interaction data are available. To improve the start-time accuracy of cold item recommendation, content-based filters [23] that exploit the similarity of side information between candidate items and the items interacted with by a target user were studied in the early era of personalized recommendation. Side information refers to metadata that describe items--such as tags, texts, or images--typically provided by item creators or sellers. It offers auxiliary signals that are strongly correlated with the latent properties of the items. Therefore, it plays a pivotal role in user interactions because it is often the first and only information that users encounter before deciding interactions with items. As a result, items with similarside information tend to elicit similar interaction patterns from users. Furthermore, side information is readily available at the time of item registration, as item providers are generally motivated to include detailed descriptions to attract user attention. Therefore, in the absence of historical interaction data, side information becomes the most critical factor for inferring the interaction patterns of new items. Although the traditional content-based filters mitigated the problem to some extent, there is still room for improvement. In the last decade, several theoretical foundations and methods that can be applicable to cold item recommendations, such as compressed sensing [24] and pairwise comparisons [25], have been proposed. Among them, methods using neural networks have been studied the most. The studies in [13; 14; 15; 16; 17; 18; 19; 20; 21; 22] tried to \"generate\" items' collaborative embeddings, which are originally learned by CFs from items' side information. These works explored various effective network architectures to achieve their goal. Specifically, these include convolutional neural networks [17] and autoencoders [13; 14; 15; 16], and parameter sharing between user and item predictors [18], techniques to prevent overfitting [19; 20], and the design of effective objective functions [20; 21; 22] are also investigated. The diffusion model (DM) [26; 27] is a powerful generative model which has shown prominent results in various domains [28; 29; 30; 31; 32]. Unlike traditional generative models like GANs and VAEs, which face stability and representation limitations, diffusion models employ a forward-noising and reverse-denoising process, aligning well with the nature of recommendation tasks where user interactions often contain noise and require reconstruction. Because of their superior generative power and denoising performance, researchers have started investigating effective ways to apply DMs in recommender systems. For instance, DMs were adopted to recommend items from the user's interaction vector [33; 34; 35; 36; 37], denoising the collaborative embeddings learned by base recommender models [38; 39], and incorporating multi-modal knowledge [40] to improve general recommendation performance.