# Diversifying Furniture Recommendations: A User-Profile-Enhanced Recommender VAE Approach
## 5 Evaluations
### Results  
The performance scores for the top-N recommendations on MovieLens-20M and the Amazon Furniture Review Dataset are presented in Table 1 and Figures 7 and 8. Additionally, the coverage on MovieLens-20M was 0.0294 for User Profile (Categories + Images), 0.0276 for User Profile (Categories), 0.0271 for User Profile (Images), and 0.0271 for the RecVAE. For the Amazon Furniture Review Dataset, the coverage was 0.5237 for User Profile (Categories + Images), 0.5031 for User Profile (Categories), 0.4956 for User Profile (Images), and 0.5035 for the RecVAE. The results on MovieLens-20M clearly show that in terms of the recommendation quality, the proposed models outperform all of the baseline models. These results indicate that the introduction of the CVAE architecture and the use of user profiles improve recommendation accuracy. In addition, the user profile created from movie genre and image information proved to be more effective in improving the recommendation accuracy. Furthermore, the proposed model with User Profile (Categories) consistently outperformed the model with User Profile (Images) across all metrics, indicating that frequency-based genre representation is more effective than image-embedding-based genre representation. This is due to the fact that each dimension of User Profile (Categories) corresponds to a specific genre, while User Profile (Images) is a less interpretable embedding of image features. Thus, the interpretability of the user profiles enhances our understanding of the user preferences and model behavior, leading to better recommendations and increased trust in the system. \begin{table}
\begin{tabular}{c c c c} \hline  & **Recall@20** & **Recall@50** & **NDCG@100** \\ \hline  & MovieLens-20M & & \\ \hline WMF [22] & 0.3836 & 0.5139 & 0.4112 \\ Multi-DAE [13] & 0.3854 & 0.5202 & 0.4129 \\ Multi-VAE [13] & 0.3879 & 0.5216 & 0.4160 \\ RaCT [23] & 0.3942 & 0.5272 & 0.4242 \\ EVCF [24] & 0.4167 & 0.5518 & 0.4451 \\ iALS [25] & 0.3880 & 0.5258 & 0.4170 \\ RecVAE [4] & 0.4103 & 0.5477 & 0.4378 \\ User Profile (Categories) & 0.4635 & 0.5851 & 0.4944 \\ User Profile (Images) & 0.4464 & 0.5723 & 0.4789 \\ User Profile (Categories + Images) & 0.4898 & 0.6039 & 0.5238 \\ \hline \multicolumn{4}{c}{Amazon Furniture Review Dataset} \\ \hline WMF [22] & 0.1640 & 0.1962 & 0.1363 \\ Multi-DAE [13] & 0.1594 & 0.2009 & 0.1310 \\ Multi-VAE [13] & 0.1524 & 0.1950 & 0.1243 \\ RaCT [23] & 0.1581 & 0.1953 & 0.1288 \\ EVCF [24] & 0.1483 & 0.1872 & 0.1318 \\ iALS [25] & 0.1719 & 0.2040 & 0.1414 \\ RecVAE [4] & 0.1589 & 0.1931 & 0.1285 \\ User Profile (Categories) & 0.1752 & 0.2233 & 0.1430 \\ User Profile (Images) & 0.1832 & 0.2284 & 0.1444 \\ User Profile (Categories + Images) & 0.2077 & 0.2619 & 0.1624 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of proposed model and baselines. Figure 7: Performance trends for different models on the MovieLens-20M dataset. In particular, User Profile (Categories + Images) outperforms other user profile approaches and shows significant improvements across all metrics when compared to the RecVAE base model, demonstrating increases of +0.0860 in NDCG@100, +0.0746 in Recall@20, and +0.0562 in Recall@50. These improvements suggest that incorporating both genres and visual information into the user profile enables our model to better capture user preferences and generate more accurate recommendations. The significant performance improvement noted when combining category and image-based profiles, as opposed to when using either one alone, can be attributed to their complementary nature--where categories provide structured taxonomic information and visual features capture subtle stylistic preferences and aesthetic patterns that often go beyond explicit categorization, resulting in a more comprehensive representation of user preferences. The particularly notable improvement in NDCG@100 indicates that our model is especially effective at ranking relevant items higher in the recommendation list, which is crucial for practical recommendation systems.