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SAR: Semantic Analysis for Recommendation

Abstract

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 SSemantic AAnalysis approach for RRecommendation systems (SAR)(SAR), which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item. SARSAR 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 SARSAR outperforms other state-of-the-art baselines substantially.

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