Diversifying Furniture Recommendations: A User-Profile-Enhanced Recommender VAE Approach

Abstract

We propose a novel recommendation model for diversifying furniture recommendations and aligning them more closely with user preferences. Our model builds upon the Recommender Variational Autoencoder (RecVAE), known for its effectiveness and ability to overcome overfitting by linking user feedback with user representation. However, since RecVAE relies on implicit feedback data, it tends to exhibit bias towards popular items, potentially creating a recommendation filter bubble. While previous work has proposed user profiles learned from a user’s personal information and the textual data of an item, we propose user profiles generated from the image data on the item given the points of interest when selecting items in e-commerce and the ease of data acquisition. We hypothesize that to capture user preferences and provide tailored furniture recommendations accurately, it is essential to incorporate both reviewed text information and visual data on furniture pieces. To utilize user preferences well, we incorporate the Conditional Variational Autoencoder (CVAE) architecture, where both the encoder and decoder are conditioned on a user profile indicating the user’s preference information. Additionally, the user profile is trained to capture the user’s preference for a specific predefined style. We trained our models using MovieLens-20M and the Amazon Furniture Review Dataset, a new dataset dedicated to furniture recommendations. As a result, on both datasets, our model outperformed previous models, including RecVAE. These findings show the effectiveness of our user profile approach in diversifying and personalizing furniture recommendations.