Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities
By Chen He, Denis Parra, Katrien Verbert. Expert systems with applications, 2016
353 citations recorded by the source corpus.
We identify shortcomings of current recommender systems.We present an interactive recommender framework to tackle the shortcomings.We analyze existing interactive recommenders along the dimensions of our framework.Based on the analysis, we identify future research challenges and opportunities. Recommender systems have been researched extensively over the past decades. Whereas several algorithms have been developed and deployed in various application domains, recent research efforts are increasingly oriented towards the user experience of recommender systems. This research goes beyond accuracy of recommendation algorithms and focuses on various human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. In this paper, we present an interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction. Then, we analyze existing interactive recommender systems along the dimensions of our framework, including our work. Based on our survey results, we present future research challenges and opportunities.
Subfields: Computer Vision and Pattern Recognition, Information Systems
View Paper: https://www.semanticscholar.org/paper/56ba8985d0d6559498322d8da7290e146783f7b2
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
- Tat-seng Chua
- Xiangnan He
- Tobias Schnabel
- Maarten de Rijke
- Susan T. Dumais
- Paul N. Bennett
- Thorsten Joachims
- Dacheng Tao
In Information Systems
- Xiangnan He
- Maarten de Rijke
- Tat-seng Chua
- Thorsten Joachims
- Susan T. Dumais
- Tobias Schnabel
- Xiang Wang
- Paul N. Bennett
Citations in the corpus
- Advances and Challenges in Conversational Recommender Systems: A Survey (2021, background): “Some studies propose interactive recommender systems [57, 176, 20, 225], which mainly focus on improving the recommendation strategy online by leveraging real-time user feedback on previous recommended items.”
- Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration (2018, background): “Lastly, we present a general interface useful for many interactive recommendation settings [17, 38].”