Explanations of recommendations

By Nava Tintarev. 2007

117 citations recorded by the source corpus.

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. Yongfeng Zhang
3. Xu Chen
4. Tat-seng Chua
5. Xiang Wang
6. Nava Tintarev
7. Fuli Feng
8. Liqiang Nie

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Yongfeng Zhang
3. Xiang Wang
4. Nava Tintarev
5. Xu Chen
6. Tat-seng Chua
7. Judith Masthoff
8. Liqiang Nie

In Information Systems

1. Yongfeng Zhang
2. Xiangnan He
3. Xu Chen
4. Tat-seng Chua
5. Xiang Wang
6. Nava Tintarev
7. Liqiang Nie
8. Judith Masthoff

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, background): “In practice, recommendation explanations can be presented in very different display styles (Tintarev and Masthoff [2015]), which could be a relevant user or item, a radar chart, a sentence, an image, etc.”
• Make it personal: A social explanation system applied to group recommendations (2017, background): “Differently to this division by design principles, explanations can be discerned by the effects they have on users (Tintarev, 2007) (see Table 1).”
• DualDiv: diversifying items and explanation styles in explainable hybrid recommendation (2019, background): “To improve the persuasiveness and user satisfaction of RSs, it is also useful to provide explanations so that users can understand why each item is recommended [13]; such approach is called ex-plainable recommendation (ER) [16].”
• Visualization of Explanations in Recommender Systems (2014, background): “The importance of a good explanation presentation is that it can better explain recommendations and can even push users to make further requests [7].”
• The FacT: Taming Latent Factor Models for Explainability with Factorization Trees (2019, background): “[28] use genre, director and cast to explain movie recommendations.”
• Collaborative Filtering Recommender Systems (2011, background): “Tintarev [144] identifies seven potential goals for explanations of recommendations: transparency of the recommender algorithm, scrutability of the results, trustworthiness of the recommendations, effectiveness for supporting decisions, persuasiveness, efficiency of decision-making, and satisfaction”
• The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data (2008, methodology): “Qualitative feedback from focus groups also shows that users vary with regard to which movie features they find important[5,6].”
• Knowledgeable Explanations for Recommender Systems (2010, background): “and others [4], [5], [6], [7] discussed the following set of characteristics that can be associated with explanations:”
• Tagsplanations: explaining recommendations using tags (2009, background): “For example, one movie recommender prototype uses movie features including genre, director, and cast to justify recommendations [14].”
• The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system (2010, background): “Thus, text is combined with image [18, 35] or sound [16] (http:// www.”
• TEM: Tree-enhanced Embedding Model for Explainable Recommendation (2018, background): “Content-based works have considered various side information, ranging from item tags [38, 40], social relationships [31, 37], contextual reviews written by users [13, 15, 28, 31, 48] to knowledge graphs [3, 8, 47].”