# Diffusion Model as a Base for Cold Item Recommendation
## 4 Evaluation Environment
### Comparison Methods  
We conducted a comparison between the proposed method and five existing related approaches.  
**DropoutNet**[19]: This approach is designed to predict recommendation scores obtained from the base recommender model by randomly masking collaborative embeddings in its input pair's CF embedding and side information embedding during training. By doing so, the model is trained to infer missing collaborative embeddings based solely on the item's side information, enabling it to generalize to cold items more effectively.  
**Heater**[20]: [PERSON] aims to directly predict collaborative embeddings by optimizing an objective function that enforces the model to learn collaborative embeddings from cold items' side information. The recommendation score is then derived from the predicted collaborative embeddings. To enhance prediction accuracy, [PERSON] employs a Mixture-of-Experts predictor and CF embedding dropout strategies.  
**CVAR**[15]: This method utilizes two encoders and a decoder within a variational autoencoder (VAE) framework [52] to transform the item's side information into collaborative embeddings. The first encoder processes pre-trained collaborative embeddings from a base CF model while the decoder reconstructs the collaborative embeddings from the encoded representations. Meanwhile, the second encoder learns representations from the item's side information. The objective function encourages the encoding from the second encoder to closely match that of the first encoder for the same item, ensuring that the decoder can generate valid collaborative embeddings, thus improving cold item recommendations.  
**CCFC**[22]: CCFC leverages contrastive learning to enhance CF embedding prediction. This method enforces similar items (e.g., those with overlapping user interaction histories) to have closely aligned collaborative embeddings while ensuring that the learned embed dings remain distinct from those of unrelated items. Through this contrastive learning strategy, CCFC refines the quality of side information-driven CF embedding predictions.  
**GME**[53]: It enhances the cold item's side information representation by aggregating information from its neighbors with similar side information using a graph attention network. The score predictor then leverages these enriched side information representations to estimate recommendation scores effectively.  
**DMCIR**: DMCIR (Diffusion Model-based Cold Item Recommender) indicates the DM-based generator of the proposed method to generate the collaborative embeddings from cold items' side information. Since it generates the collaborative embeddings of the item side, it can recommend items to users by calculating dot product between the generated collaborative embeddings of candidate items and the collaborative embeddings of target users.  
**DMCIR+**: DMCIR+ represents the results that applied the Refiner of the proposed method to the collaborative embeddings generated by DMCIR.  
The methods were implemented and evaluated using TensorFlow 2.15 ([[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/)) (accessed on 30 March 2025)) on a machine equipped with a Ryzen Threadripper Pro 3945 WX 12-core CPU (AMD), 128 GB of RAM (Samsung, Suwon Republic of Korea), and a TITAN RTX GPU (nVidia, Santa Clara, CA, USA). The source code for the proposed method is publicly available at [[https://github.com/JungkyuHan/DMCIR](https://github.com/JungkyuHan/DMCIR)]([https://github.com/JungkyuHan/DMCIR](https://github.com/JungkyuHan/DMCIR)) (accessed on 30 March 2025). For the implementation of comparison methods, when the official source codes were available [15; 19; 20; 22], we referred to them. Additionally, we adopted the hyperparameter settings that yielded the best validation performance. We evaluated the models with different initial model parameters five times and reported the average performance. DMCIR/DMCIR+ used the hyperparameters shown in Table 3.