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A survey on multi-objective recommender systems.
Dietmar Jannach1, Himan Abdollahpouri2
1Department of Artificial Intelligence and Cybersecurity, University of Klagenfurt, Klagenfurt, Austria.
Frontiers in Big Data
|April 10, 2023
Summary
Recommender systems often need to balance multiple competing goals beyond just accuracy. This review explores various types of multi-objective recommendation challenges and identifies key research areas.
Area of Science:
- Computer Science
- Artificial Intelligence
- Information Retrieval
Background:
- Recommender systems traditionally focus on single-objective accuracy using machine learning.
- Real-world applications require balancing multiple, often conflicting, objectives for effective content delivery.
Purpose of the Study:
- To review different types of multi-objective recommendation settings.
- To outline open challenges in the field of multi-objective recommender systems.
Main Methods:
- Literature review of multi-objective recommendation research.
- Categorization of competing objectives in recommender systems.
Main Results:
- Identified five key types of competing objectives: recommendation quality, stakeholder interests, short-term vs. long-term goals, user interface considerations, and engineering constraints.
- Highlighted the insufficiency of single-objective optimization in practical recommender systems.
Conclusions:
- Multi-objective optimization is crucial for advancing recommender system performance.
- Further research is needed to address the identified open challenges in multi-objective recommender systems.
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