Recommendation System—Explainability, Evaluation and Rank with User Behavior Dynamics
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 336
Special Issue Editors
Interests: unsupervised learning; anomaly detection; recommendation systems
Interests: machine learning applications; interactive and pervasive computing; fault diagnosis and prognosis
Interests: medical information retrieval; interactive machine learning; computational terminology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recommendation systems have been becoming increasingly more essential in people’s lives. Hence, more research and development efforts have been invested by researchers in academic institutes and R&D departments in various businesses.
The application of effective recommendation systems is present in many aspects of people’s day-to-day lives, such as being used by Netflix, YouTube, and TikTok users to help decide which movie or video to watch, Spotify for letting people know what music to listen to, LinkedIn for discovering matched candidates or jobs, and Stitch Fix for becoming your personalized stylist. At the same time, people can almost search for anything on Google, and it will recommend relevant information. Meanwhile, recommendation systems are still facing a series of challenges. In this Special Issue, we would like to see topics such as model explainability, evaluation, and learning to rank with the dynamics of user behaviors.
Model explainability: Addressing the problem of the building transparency of models, increasing the maintainability of models, and enhancing the decision-making process for end users. In recent years, explainability in AI has grown in importance for various reasons, such as fairness and transparency. It is specifically meaningful in recommendation systems as it helps system developers to understand the system better and, hence, can efficiently improve the system. It also can potentially better engage the end users by providing context as to why an item is recommended.
Model evaluation: Addressing the challenge of data sparsity, such as how to collect feedback for evaluation, how to use limited data for assessments, and how to choose which metrics to optimize in various scenarios.
Learning to rank with the dynamics of user behaviors: First, we need to address the general ranking problem—what is the first item a system would want the user to see or use; hence, bringing more user engagement and satisfaction. Second, how should ranking mechanisms be captured and adapted to changed use behaviors.
The above is a shortlist of recommended focus areas. However, in this Special Issue, topics are not limited to those mentioned above. We encourage researchers and developers to share and submit manuscripts on a broader scope of recommendations systems. In addition to algorithm improvements and vision expansions, domain-specific applications are also welcome.
Dr. Qi Liu
Dr. Yingying Zhao
Dr. Giorgio Maria Di Nunzio
Guest Editors
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Keywords
- recommendation system
- deep learning
- machine learning
- model explainability
- model evaluation
- applications
