A Capsule Network for Recommendation and Explaining What You Like and Dislike

By Chenliang Li, Cong Quan, Liwei Peng, Yunwei Qi, Yuming Deng, Libing Wu. 2019

129 citations recorded by the source corpus.

User reviews contain rich semantics towards the preference of users to features of items. Recently, many deep learning based solutions have been proposed by exploiting reviews for recommendation. The attention mechanism is mainly adopted in these works to identify words or aspects that are important for rating prediction. However, it is still hard to understand whether a user likes or dislikes an aspect of an item according to what viewpoint the user holds and to what extent, without examining the review details. Here, we consider a pair of a viewpoint held by a user and an aspect of an item as a logic unit. Reasoning a rating behavior by discovering the informative logic units from the reviews and resolving their corresponding sentiments could enable a better rating prediction with explanation. To this end, in this paper, we propose a capsule network based model for rating prediction with user reviews, named CARP. For each user-item pair, CARP is devised to extract the informative logic units from the reviews and infer their corresponding sentiments. The model firstly extracts the viewpoints and aspects from the user and item review documents respectively. Then we derive the representation of each logic unit based on its constituent viewpoint and aspect. A sentiment capsule architecture with a novel Routing by Bi-Agreement mechanism is proposed to identify the informative logic unit and the sentiment based representations in user-item level for rating prediction. Extensive experiments are conducted over seven real-world datasets with diverse characteristics. Our results demonstrate that the proposed CARP obtains substantial performance gain over recently proposed state-of-the-art models in terms of prediction accuracy. Further analysis shows that our model can successfully discover the interpretable reasons at a finer level of granularity.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Xu Chen
5. Yongfeng Zhang
6. Chang Zhou
7. Hongxia Yang
8. Hongye Jin

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Xu Chen
5. Yongfeng Zhang
6. Liqiang Nie
7. Hongye Jin
8. An Zhang

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, background): “, 2019b), capsule networks (Li et al., 2019), and many others.”
• Disentangled Graph Collaborative Filtering (2020, background): “history with users [21], item knowledge [15, 32, 39, 41, 42], and user reviews [5, 22], or conduct psychology experiments [24] to establish the ground truth on user intents and further interpret disentangled representations better.”
• Controllable Multi-Interest Framework for Recommendation (2020, background): “CARP [32] firstly extracts the viewpoints and aspects from the user and item review documents and derives the representation of each logic unit based on its constituent viewpoint and aspect for rating prediction.”
• Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond (2021, background): “Recent works on explainable recommendation systems can be categorized following our proposed taxonomy, and most of them focus on designing interpretable modules [36, 102, 147, 162].”