How Much Do Reviews Really Contribute? A Study on Text-Enriched Matrix Factorization for Recommendations
arXiv:2606.16973v1 Announce Type: cross Abstract: Incorporating textual reviews into a Recommender System has become a prominent strategy for enriching collaborative signals with semantic information. However, the actual contribution of review-derived representations remains an open question, particularly when strong collaborative baselines are employed. In this work, we systematically investigate the impact of textual information on Matrix Factorization by introducing and comparing three enrichment strategies over a common collaborative backbone. First, we propose a learnable gating mechanism
The proliferation of digital content and e-commerce has led to an explosion of user-generated reviews, making their effective integration into recommender systems a critical area of research.
Improving the accuracy and relevance of recommender systems can significantly impact user engagement, sales, and platform stickiness across various online services.
This research provides a more nuanced understanding of how textual reviews contribute to matrix factorization models, potentially leading to more efficient and effective recommendation algorithms.
- · E-commerce platforms
- · Content streaming services
- · AI researchers (Recommender Systems)
- · Online service providers
- · Inefficient recommender systems
- · Users receiving irrelevant recommendations
More accurate product and content recommendations for users.
Increased user satisfaction and conversion rates on platforms leveraging these improvements.
Enhanced competitive advantage for companies that effectively integrate advanced text-enriched recommendation techniques.
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Read at arXiv cs.AI