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Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application
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DOI：10.1109/ACCESS.2020.3028777.png)
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Abstract
En 中文
Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conflicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be holistic i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: first, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best fit the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of holistic RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles.
Keywords:
Recommender systems
Decision making
Mood
Knowledge based systems
Complexity theory
Cognition
Encoding
Theoretical framework
design methodology
recommender systems
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