Explaining neighborhood-based recommendations

By Sergio Cleger-Tamayo, Juan M. Fernández-Luna, Juan F. Huete. 2012

16 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Yongfeng Zhang
2. Xu Chen
3. Kenny Q. Zhu
4. Xiangnan He

In Information Systems

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

In Signal Processing

1. Yongfeng Zhang
2. Kenny Q. Zhu
3. Xu Chen

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “[2001], Zanker and Ninaus [2010], Cleger-Tamayo et al. [2012]), which are two fundamental methods for personalized recommendation.”
• AliCoCo: Alibaba E-commerce Cognitive Concept Net (2020, background): “While explainable recommendation attracts much research attention recently [33], most existing works are not practical enough for industry systems, since they are either too complicated (based on NLG [8, 32]), or too trivial (e.”
• A systematic review and taxonomy of explanations in decision support and recommender systems (2017, unknown): “Effectiveness Non-personalised Decisive Features (Personalised, Popularity): Cameras [224] (Non-)personalised Tag-based (Keyword-based) [68] (Non-)personalised Tag-based (Popularity, Neighbours) [69] Predicted Rating (Popularity, Single Feature) [87] Trace (Justification, Strategy) [217] Visualisati”