Deep transfer learning with multimodal embedding to tackle cold-start and sparsity issues in recommendation system.

ABSTRACT: Recommender systems (RSs) have become increasingly vital in the modern information era and connected economy. They play a key role in business operations by generating personalized suggestions and minimizing information overload. However, the performance of traditional RSs is limited by data sparseness and cold-start issues. Though deep learning-based recommender systems (DLRSs) are very popular, they underperform when considering rating matrices with sparse entries. Despite their performance improvements, DLRSs also suffer from data sparsity, cold start, serendipity, and generalizability issues. We propose a multistage model that uses multimodal data embedding and deep transfer learning for effective and personalized product recommendations, and is designed to overcome data sparsity and c

SUBMITTER: Jafri SIH 

PROVIDER: S-EPMC9410545 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature