DualDiv: diversifying items and explanation styles in explainable hybrid recommendation

By Kosetsu Tsukuda, Masataka Goto. 2019

15 citations recorded by the source corpus.

In recommender systems, item diversification and explainable recommendations improve users' satisfaction. Unlike traditional explainable recommendations that display a single explanation for each item, explainable hybrid recommendations display multiple explanations for each item and are, therefore, more beneficial for users. When multiple explanations are displayed, one problem is that similar sets of explanation styles (ESs) such as user-based, item-based, and popularity-based may be displayed for similar items. Although item diversification has been studied well, the question of how to diversify the ESs remains underexplored. In this paper, we propose a method for diversifying ESs and a framework, called DualDiv, that recommends items by diversifying both the items and the ESs. Our experimental results show that DualDiv can increase the diversity of the items and the ESs without largely reducing the recommendation accuracy.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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. Xu Chen
2. Yongfeng Zhang
3. Xiangnan He
4. Zheng Qin
5. Tat-seng Chua
6. Qingyao Ai
7. Xiang Wang
8. Hanxiong Chen

In Information Systems

1. Xu Chen
2. Yongfeng Zhang
3. Xiangnan He
4. Zheng Qin
5. Tat-seng Chua
6. Qingyao Ai
7. Xiang Wang
8. Hanxiong Chen

In Management Science and Operations Research

1. Yongfeng Zhang
2. Xu Chen

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, background): “On one hand, researchers have shown that providing diversified explanations is beneficial to user satisfaction in recommender systems (Tsukuda and Goto, 2019).”