# Diffusion Model as a Base for Cold Item Recommendation
## 4 Evaluation Environment
### Datasets  
We evaluated the proposed method with two real-world datasets, CiteULike and MovieLens25M.  
**CiteULike**[63] is a well-known dataset used for research in recommender systems. The data were collected from CiteULike, an online reference management and social bookmarking platform where users can save and share academic papers. The dataset includes about 0.2 million user-save-article interactions. As side information, abstracts of the articles are provided. We followed [19; 20; 63] to calculate the embeddings for side information. Specifically, we first selected the top 8000 high-scored words from the wordsthat appeared in the abstract corpus by applying tf-idf and constructed an 8000-dimensional multi-hot feature vector for each paper. After that, the multi-hot vectors were deduced to 300-dimensional embedding vectors by applying SVD [64]. We randomly selected 1018 and 2378 non-interacted items as the validation and test item sets, and interactions related to the two item sets were used as validation and test sets, respectively. The remainder of the interactions were used as the training set.  
**MovieLens25M (ML25M)**[65] is a dataset for movie recommendation research, released by the Grouplens ([[https://grouplens.org/](https://grouplens.org/)]([https://grouplens.org/](https://grouplens.org/)) (accessed on 30 March 2025)). It contains 25 million user ratings and movie metadata and is one of the largest publicly available datasets. The dataset includes side information such as titles, genres, release years, and additional tagging information. We calculated the 400-dimensional side information embedding from the tag genome data [66] of each movie by applying VAE [52]. For train-validation-test data separation, we made five non-intersected movie groups of the same size. We selected one group and used 70% of its movies as the test movie set and the remaining 30% as the validation movie set. The last four movie groups were used as the train movie set. The interaction data associated with the three movie sets were used as the test, validation, and training sets. From all sets, ratings of less than 3.0 in 5-scale ratings were filtered out as negative ratings. We performed 5-fold cross-validation by selecting another group for the test and validation groups.  
**Collaborative embedding of base CF method:** We used BPR [67], one of the most popular CF algorithms in academia and industry, as the base CF method to compute the collaborative embeddings. We calculated the collaborative embeddings of dimensions 200 and 128 using the training dataset for CiteULike and ML25M, respectively. Table 2 represents the statistics of the datasets.  
**Quality of side information embeddings:** We used SVD and VAE to obtain compact embeddings for words and tag distributions, respectively. Both methods are optimized to minimize the reconstruction error of the original word or tag distribution of an item from the embedding. To investigate whether severe compression could result in information loss, we conducted experiments by varying the embedding dimensions. We observed that increasing the dimensionality beyond the values reported in the description of CiteULike and ML25M did not lead to significant improvements in reconstruction performance. This suggests that the chosen dimensions are reasonable balances between compression and information preservation.