SAR: Semantic Analysis for Recommendation
This addresses the need for more interpretable and semantically rich recommendations in systems like e-commerce or streaming services, though it appears incremental as it builds on existing matrix completion techniques.
The paper tackles the problem of weak semantic interpretation in recommendation systems by proposing SAR, a semantic analysis approach that learns semantic representations from user ratings and matches them for recommendations, achieving substantial performance improvements over state-of-the-art baselines.
Recommendation system is a common demand in daily life and matrix completion is a widely adopted technique for this task. However, most matrix completion methods lack semantic interpretation and usually result in weak-semantic recommendations. To this end, this paper proposes a $S$emantic $A$nalysis approach for $R$ecommendation systems $(SAR)$, which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item. $SAR$ learns semantic representations of users/items merely from user ratings on items, which offers a new path to recommendation by semantic matching with the learned representations. Extensive experiments demonstrate $SAR$ outperforms other state-of-the-art baselines substantially.