AspeRa: Aspect-Based Rating Prediction Model
| Autor: | Ilya Shenbin, Anton Alekseev, Sergey I. Nikolenko, Valentin Malykh, Elena Tutubalina |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning 02 engineering and technology Recommender system Machine learning computer.software_genre 020204 information systems 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Joint (building) Artificial intelligence Architecture business computer Real world data Predictive modelling |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030157180 ECIR (2) |
| Popis: | We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users. The AspeRa model uses max-margin losses for joint item and user embedding learning and a dual-headed architecture; it significantly outperforms recently proposed state-of-the-art models such as DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews. With qualitative examination of the aspects and quantitative evaluation of rating prediction models based on these aspects, we show how aspect embeddings can be used in a recommender system. |
| Databáze: | OpenAIRE |
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