Multi-BERT for Embeddings for Recommendation System

TL;DR

This paper introduces a novel Multi-BERT approach combining SBERT and RoBERTa to generate more semantically rich document embeddings, improving recommendation accuracy in a book recommendation task.

Contribution

The paper presents a new Multi-BERT model that enhances document embeddings by integrating SBERT and RoBERTa, capturing intra- and inter-sentence relations for better recommendations.

Findings

Multi-BERT outperforms SBERT in embedding quality

Enhanced embeddings capture nuanced semantic relations

Improved recommendation accuracy on Goodreads dataset

Abstract

In this paper, we propose a novel approach for generating document embeddings using a combination of Sentence-BERT (SBERT) and RoBERTa, two state-of-the-art natural language processing models. Our approach treats sentences as tokens and generates embeddings for them, allowing the model to capture both intra-sentence and inter-sentence relations within a document. We evaluate our model on a book recommendation task and demonstrate its effectiveness in generating more semantically rich and accurate document embeddings. To assess the performance of our approach, we conducted experiments on a book recommendation task using the Goodreads dataset. We compared the document embeddings generated using our MULTI-BERT model to those generated using SBERT alone. We used precision as our evaluation metric to compare the quality of the generated embeddings. Our results showed that our model consistently outperformed SBERT in terms of the quality of the generated embeddings. Furthermore, we found that our model was able to capture more nuanced semantic relations within documents, leading to more accurate recommendations. Overall, our results demonstrate the effectiveness of our approach and suggest that it is a promising direction for improving the performance of recommendation systems