# Diversifying Furniture Recommendations: A User-Profile-Enhanced Recommender VAE Approach
## 6 Discussions  
In this section, we analyze the experimental results and discuss the impact of our proposed method. We examine the architectural choices, the effect of the user profile generation method, the performance of the user profile, and the impact of style specification.  
Adopting a CVAE architecture for recommendations allows for flexible encoder structuring tailored to different datasets. Our approach enhances the top-N recommendations by incorporating user preference data as a conditioning factor. The integration of a user profile--derived from category and image data--demonstrates the advantage of this combined approach over using either representation independently.  
Our findings indicate that incorporating an interpretable user profile significantly improves the recommendation accuracy. The combination of image embeddings with categorical style representations outperforms single-modality profiles. This result highlights the importance of interpretability in capturing user preferences, leading to better recommendations and enhanced user trust. Additionally, since our user profile was generated from the final softmax layer of ResNet50, it was possible to include multiple styles within the same user profile. This flexibility allows the model to represent a broader range of user preferences more accurately and helps mitigate the redundant recommendation problem by promoting diversity in the suggested items.  
While our approach shows promising results, we acknowledge ethical considerations and potential limitations. Using image data for profiling raises privacy concerns, as inferred style preferences may indirectly reveal sensitive user information. Ensuring transparent data handling and obtaining user consent are crucial.  
Additionally, dataset biases may influence the recommendation fairness. If the training data lack diversity in their furniture styles or cultural representations, the recommendations may not adequately serve all user groups. Addressing this issue requires diverse and representative datasets. Furthermore, the transferability of our approach across domains is inherently problem-dependent. While our method demonstrates a strong performance with furniture data that contain well-structured category information and rich visual features, its effectiveness may vary significantly when applied to domains with different characteristics. For instance, domains like music or abstract art might pose challenges due to their less concrete visual semantics or more subjective categorization. Similarly, domains with minimal structured metadata would require substantial adaptations to our framework. The balance between relying on categorical information and visual features must be calibrated according to the specific properties of the target domain and the quality of available data. This domain dependency highlights the need for careful evaluation and potential methodology adjustments when expanding beyond the furniture recommendation context.  
Our method also assumes that user preferences can be inferred from image data alone. However, factors such as price, brand, and functionality--currently not incorporated--may significantly impact purchasing decisions [27; 28; 29]. Future work could integrate these factors for a more comprehensive profiling approach. Additionally, the CNN utilized to generate user profiles necessitates choosing the optimal model based on the domain and dataset. Thus, we believe that a mechanism for selecting the best model according to the domain and dataset will be essential in the future.  
Finally, while our method promotes diversity, there is a risk of reinforcing user preferences and limiting exposure to alternative styles (i.e., filter bubbles). Balancing personalization with diversity remains an ongoing challenge in recommendation systems.