Research on the influence of interpretability of artificial intelligence recommendation system on users' behavior intention

Wang, GaoShan; Liu, XiQuan; Wang, ZhongGuo; Yang, Xuelan

doi:10.1145/3443467.3443850

Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering 2020

DOI: 10.1145/3443467.3443850

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Research on the influence of interpretability of artificial intelligence recommendation system on users' behavior intention

GaoShan Wang

XiQuan Liu

ZhongGuo Wang

et al.

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2023

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A Personalized Explainable Learner Implicit Friend Recommendation Method

Zhou

Lin

et al. 2023

Data Sci. Eng.

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With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability.

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A Personalized Explainable Learner Implicit Friend Recommendation Method

Zhou

Lin

et al. 2023

Data Sci. Eng.

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show abstract

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Research on the influence of interpretability of artificial intelligence recommendation system on users' behavior intention

Cited by 1 publication

References 10 publications

A Personalized Explainable Learner Implicit Friend Recommendation Method

A Personalized Explainable Learner Implicit Friend Recommendation Method

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