Diversifying Furniture Recommendations: A User-Profile-Enhanced Recommender VAE Approach
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Image Retrieval and Classification Techniques · Video Analysis and Summarization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Master’s Program in Information and Computer Science, Doshisha University, Kyoto 610-0394, Japan
Department of Intelligent Information Engineering and Sciences, Doshisha University, Kyoto 610-0394, Japan
Department of Information and Electronic Engineering, International Hellenic University, 14th km Thessaloniki–N. Moudania National Road, 57001 Thermi, Greece
Funding
JSPS KAKENHI
References (32)
Show source-provided references
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