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Considering temporal aspects in recommender systems: a survey
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PRE
AI
DOI：10.1007/s11257-022-09335-w.png)
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Abstract
En 中文
The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.
Keywords:
Recommender systems
User modeling
Temporal aspects
Dynamics
Long-term preferences
Short-term preferences
Survey
Journal
U
IF:
3.5
Papers:
532
Citations:
1.7K
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