Visually Explainable Recommendation

By Xu Chen, Yongfeng Zhang, Hongteng Xu, Yixin Cao, Zheng Qin, Hongyuan Zha. 2018

54 citations recorded by the source corpus.

Images account for a significant part of user decisions in many application scenarios, such as product images in e-commerce, or user image posts in social networks. It is intuitive that user preferences on the visual patterns of image (e.g., hue, texture, color, etc) can be highly personalized, and this provides us with highly discriminative features to make personalized recommendations. Previous work that takes advantage of images for recommendation usually transforms the images into latent representation vectors, which are adopted by a recommendation component to assist personalized user/item profiling and recommendation. However, such vectors are hardly useful in terms of providing visual explanations to users about why a particular item is recommended, and thus weakens the explainability of recommendation systems. As a step towards explainable recommendation models, we propose visually explainable recommendation based on attentive neural networks to model the user attention on images, under the supervision of both implicit feedback and textual reviews. By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner. Experimental results show that our models are not only able to improve the recommendation performance, but also can provide persuasive visual explanations for the users to take the recommendations.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 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. Yongfeng Zhang
5. Xu Chen
6. Xing Xie
7. Le Wu
8. Yixin Cao

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Yongfeng Zhang
3. Xiang Wang
4. Xu Chen
5. Tat-seng Chua
6. Yi Tay
7. Le Wu
8. Xing Xie

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Yongfeng Zhang
5. Xu Chen
6. Le Wu
7. Xing Xie
8. Yixin Cao

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, background): “Recently, there has been some related work trying to generate natural language explanations, and the basic idea is to train sequence-tosequence models based on user reviews, and to generate “review-like” sentences as explanations of the recommendation, such as Costa et al. [2017] and Chen et al.”
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences (2019, background): “ility to understand why an item is being recommended out of others. This has been found to be important for the effectiveness, efficiency, persuasiveness, and user satisfaction of recommender systems [8, 39, 47]. Among the side information, knowledge graphs (e.g., DBPedia [20]) show great potentials”
• A Reinforcement Learning Framework for Explainable Recommendation (2018, background): “Except for collaborative filtering, researchers have also proposed explanation strategies for deep-learningbased methods [12]–[14].”
• DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020, background): “Post-hoc explainability (Samek et al., 2017; Wang et al., 2018; Chen et al., 2018) needs to de-sign a second model to provide explanations for an existing model.”
• Deep Learning Based Recommender System (2017, background): “Many other mediums/modalities can be considered, such as images [18].”
• Personalized Reason Generation for Explainable Song Recommendation (2019, background): “The features can also be displayed in different forms [47–49].”
• Transparent, Scrutable and Explainable User Models for Personalized Recommendation (2019, background): “Various forms of explanations have been explored in prior work, including sentences [21, 55], tag/keyword clouds [17, 30, 53], as well as different kinds of visualizations [10, 22, 31, 47].”