AspeRa: Aspect-Based Rating Prediction Based on User Reviews
Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, Ilya Shenbin
Abstract
We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)- Anthology ID:
- W19-3605
- Volume:
- Proceedings of the 2019 Workshop on Widening NLP
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Amittai Axelrod, Diyi Yang, Rossana Cunha, Samira Shaikh, Zeerak Waseem
- Venue:
- WiNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11–13
- Language:
- URL:
- https://aclanthology.org/W19-3605
- DOI:
- Cite (ACL):
- Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, and Ilya Shenbin. 2019. AspeRa: Aspect-Based Rating Prediction Based on User Reviews. In Proceedings of the 2019 Workshop on Widening NLP, pages 11–13, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- AspeRa: Aspect-Based Rating Prediction Based on User Reviews (Tutubalina et al., WiNLP 2019)