Recommendation Diversification Using Explanations

By Cong Yu, Laks V. S. Lakshmanan, Sihem Amer-Yahia. 2009

79 citations recorded by the source corpus.

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. Mahdi Jalili
4. Amin Javari
5. Xiangnan He
6. Zheng Qin
7. Tat-seng Chua
8. Qingyao Ai

In Information Systems

1. Xu Chen
2. Yongfeng Zhang
3. Mahdi Jalili
4. Amin Javari
5. Xiangnan He
6. Zheng Qin
7. Tat-seng Chua
8. Qingyao Ai

In Management Science and Operations Research

1. Mahdi Jalili
2. Yongfeng Zhang
3. Amin Javari
4. Xu Chen
5. Tomaz Pozrl
6. Matevz Kunaver

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “For example, Zhang et al. [2014a] generated (personalized) textual sentences as recommendation explanation to help users understand each recommendation result; McAuley and Leskovec [2013], Zhang [2015], Al-Taie and Kadry [2014] provided topical word cloud or clus-”
• A probabilistic model to resolve diversity–accuracy challenge of recommendation systems (2015, background): “Novelty and diversity in recommender systems recently received much attention [3,6,33, 35,36].”