# Diffusion Model as a Base for Cold Item Recommendation
## 2 Related Work
### Cold Item Recommendation  
Studies on cold-start recommendation (for both users and items) can be broadly classified into two categories. The first group of studies [5, 6, 7, 8, 9, 10, 11, 12] focuses on fast warm-up, which aims to achieve accurate recommendations using minimal interaction data. To achieve their goal, they adopted meta-learning [5, 6]. Some studied the fast warm-up of cold users by training per-user recommenders [7], adopting memory structures [8], or using task-adaptive mechanisms [9] to effectively re-use information for fast adaptation. Other studies [10, 11, 12, 47] studied cold item warm-up. [PERSON] et al. [10] learned item embeddings that encode the interaction characteristics of items by meta-learning. [PERSON] et al. [11] adopted a Heterogeneous Information Network (HIN) [48, 49] to improve the performance of meta-learning. [PERSON] et al. [12] updated collaborative embeddings of cold items using meta-scaling and meta-shift. [PERSON] et al. [47] trained multiple meta-learners to exploit different types of item contents, such as review text and images, to improve the next-item recommendation performance.  
Another group of studies tried to increase the start-time recommendation accuracy of cold items by using side information. Since the cold item problem arises from a lack of interaction data, knowledge propagation on graphs can be one of the solutions to augment deficient user-item interaction data. [PERSON] et al. [50] improved cold item recommendation performance in cross-domain settings by transferring knowledge from a source domain graph with sufficient interactions and side information to a target domain graph that contains only side information. [PERSON] et al. [51] generated pseudo-user-item interactions based on the strength of semantic relations calculated with a knowledge graph.  
As another approach, methods that tried to predict collaborative embeddings of cold items based on their side information were studied. Various neural network architectures suitable for collaborative embedding predictors were investigated to achieve accurate prediction. For music recommendation, [PERSON] et al. [13] integrated the gravity model into an attribute graph connecting similar artists and side musical information for graph autoencoder to suggest cold artists to users, and [PERSON] et al. [17] evaluated a CNN architecture for music embedding generation from side information such as track and artist information. [PERSON] et al. [15] trained two variational autoencoders [52], the first encoder to encode the item collaborative embeddings and the second encoder to generate the collaborative embeddings encoded by the first encoder based on the side information of cold items. [PERSON] et al. [16] used an autoencoder to predict collaborative embeddings, represented as binary hash codes, from the side information of cold items. [PERSON] et al. [18] reduced model training time by sharing the parameters of the first layer between the item and user collaborative embedding predictors.  
In addition to the effective network structure, other factors, such additional information and training techniques, are also investigated. [PERSON] et al. [53] suggested a side information adaptation using the side information of the content neighbors of cold items. [PERSON] et al. [19] proposed an effective training method for collaborative embedding predictors by adopting a dropout technique. [PERSON] et al. [20] showed that directly reducing the error of collaborative embedding predictor by explicitly defining the error in the objective function can improve cold item recommendation performance. [PERSON] et al. [21] and [PERSON] et al. [22] adopted contrastive learning [54] techniques to train more accurate cold item recommender models.