How Much Do Reviews Really Contribute? A Study on Text-Enriched Matrix Factorization for Recommendations
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 that adaptively balances collaborative and textual signals during training. This mechanism is applied to two distinct review representations: (i) ...
| Authors | Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires Denis Dantas Boaventura et al. |
|---|---|
| Primary category | cs.IR |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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