# Diversifying Furniture Recommendations: A User-Profile-Enhanced Recommender VAE Approach
## 1 Introduction  
The growing demand for effective product recommendation systems in e-commerce [1, 2] poses an important challenge: how to capture and integrate user preferences into recommendations accurately. Extracting user preferences through modeling is a challenging task, particularly in the context of furniture e-commerce due to the inherent complexities of the products themselves [3]. Furniture is characterized by diverse styles, complex shapes, intricate textures, and a wide range of colors, all of which significantly influence user preferences. Traditional recommendation methods, such as Matrix Factorization (MF), struggle to capture the full breadth of these preferences, often resulting in suboptimal user experiences where the recommended items fail to align with personal tastes. While MF serves as a common foundation for collaborative filtering, it faces several challenges,including a large number of parameters that slow down the model learning and lead to overfitting. Overfitting can also occur when users or items have only a small number of ratings. Additionally, an optimization procedure must be performed to predict the preferences for new users or items, further complicating the recommendation process in the dynamic environment of furniture e-commerce. Studies that adapt autoencoders to collaborative filtering have addressed some of these challenges, predicting new user-item ratings without repetitive training and demonstrating an improved performance on collaborative filtering datasets. Among these models, the Recommender Variational Autoencoder (RecVAE) [4] has been reported to outperform other models in its top-N recommendations. However, the RecVAE relies on implicit feedback data, which can lead to bias towards popular items [5], resulting in a recommendation filter bubble [6] and an unpleasant and redundant user experience. To address this issue, researchers have explored Conditional Variational Autoencoder (CVAE) architectures that incorporate textual information such as item category information and descriptions [7] or integrate user personal information along with item content data [8]. Additionally, there is research that has processed user feedback using Large Language Models (LLMs) to generate summaries, which were then learned by VAEs to create recommendations [9]. However, these models do not yet fully account for the preferences of individual users. In addition, the scope of the application is limited due to the requirement for users to disclose personal information. Furthermore, in e-commerce, product images often carry more weight in decision-making than product titles or summaries [10]. We hypothesize that user preferences for furniture recommendations are more strongly influenced by product images--specifically, attributes like style (genre), shape, and texture--than by text or personal information. In addition, since product images are more readily available than users' personal information, methods utilizing product images can be applied across a broader range of contexts. In response to these challenges, we propose a novel recommendation model for improving the accuracy of furniture recommendations by incorporating user preferences derived from images. We employ the RecVAE as the base model due to its reported effectiveness in estimating user behavior through latent space [4]. This study introduces a novel approach by incorporating a user profile into the RecVAE to enhance the accuracy of its furniture recommendations. Specifically, the user profile, derived from item images using a trained CNN model, assigns a probability value to the furniture styles based on user evaluations. This profile is then integrated into both the encoder and decoder inputs, enabling the model to capture additional features beyond traditional feedback data. We conducted experiments using two datasets: MovieLens-20M and the Amazon Furniture Review Dataset. MovieLens-20M (ML-20M) consists of 20 million ratings applied to more than 20,000 movies by 136,000 users.