# Diversifying Furniture Recommendations: A User-Profile-Enhanced Recommender VAE Approach
## 2 Related Work  
User preferences play a crucial role in item recommendation systems. MF and Variational Autoencoders (VAEs) commonly employ techniques to incorporate user preferences. VAEs have recently gained popularity for their ability to efficiently perform collaborative filtering and impute missing values in user-item feedback data [11], making them one of the most popular recommendation methodologies [12]. In particular, the RecVAE has demonstrated a superior performance on various collaborative filtering datasets by using a parameterized function that maps user feedback to user embeddings [4]. However, a significant drawback of VAEs lies in their strong bias toward popular items, leading to repetitive recommendations of commonly endorsed items, even when more relevant alternatives exist in the catalog [5]. To address this issue, researchers have explored the CVAE architecture, which enables semi-supervised dimensionality reduction by assigning correct labels to the encoder and decoder of the VAE.  
[PERSON] et al. [7] demonstrated that incorporating textual information, such as item categories and descriptions, into CVAE leads to richer feature representations and significantly improves the accuracy in highly sparse data. In a similar study, [PERSON] et al. [8] improved the accuracy by combining user personal information and item content data in the encoder and decoder inputs. Additionally, [PERSON] et al. [9] processed user feedback using LLMs to generate summaries, which were then combined in the VAE latent space to produce highly accurate recommendations. These studies demonstrated the effectiveness of incorporating user preference information (the user profile) into a VAE based on the CVAE architecture, utilizing text representing the item characteristics and users' personal information.  
However, practical limitations arise in real e-commerce settings where obtaining text-based item characteristics or user personal information in advance may not always be feasible due to privacy concerns, data quality issues, and the complexity of extracting relevant information from unstructured text data. Data protection regulations often limit the collection and use of personal information, and obtaining explicit user consent can be challenging. Additionally, text-based data may contain noise, inaccuracies, or irrelevant information, making it difficult to extract meaningful features. Moreover, extracting relevant information from unstructured text data requires advanced NLP techniques, which can be computationally expensive and time-consuming.  
To address these challenges, our study proposes a novel method for creating a user profile using images of items. While the existing methods have focused on text-based user preferences or explicit personal information, our approach leverages product images to infer user preferences--specifically on furniture styles. This method not only enhances the accuracy of the item recommendations but also eliminates the need to collect sensitive personal information, making it more applicable in privacy-sensitive contexts. Furthermore, we introduce a new model that integrates this image-based user profile into the RecVAE,aiming to improve the recommendation accuracy. This approach is scalable and represents an underexplored area in the context of recommendation systems, particularly for furniture items where, visual attributes play a crucial role in user preferences.