Fairness-Aware Explainable Recommendation over Knowledge Graphs

Fu, Zuohui; Xian, Yikun; Gao, Ruoyuan; Zhao, Jieyu; Huang, Qi‐Fang; Ge, Yingqiang; Xu, Shuyuan; Geng, Shijie; Shah, Chirag; Zhang, Yongfeng; Melo, Gerard de

doi:10.1145/3397271.3401051

Cited by 152 publications

(84 citation statements)

References 36 publications

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“…Ge et al [16] explore long-term fairness in recommendation and accomplish the problem through dynamic fairness learning. Fu et al [15] propose a fairness constrained approach to mitigate the unfairness problem in the context of explainable recommendation over knowledge graphs. They find that performance bias exists between different user groups, and claim that such bias comes from the different distribution of path diversity.…”

Section: Fair Recommendationmentioning

confidence: 99%

User-oriented Fairness in Recommendation

Chen

et al. 2021

Proceedings of the Web Conference 2021

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154

141

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As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it is important to identify and solve the unfairness issues in recommendation scenarios.In this paper, we address the unfairness problem in recommender systems from the user perspective. We group users into advantaged and disadvantaged groups according to their level of activity, and conduct experiments to show that current recommender systems will behave unfairly between two groups of users. Specifically, the advantaged users (active) who only account for a small proportion in data enjoy much higher recommendation quality than those disadvantaged users (inactive). Such bias can also affect the overall performance since the disadvantaged users are the majority. To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics. The experiments we conducted on several real-world datasets with various recommendation algorithms show that our approach can not only improve group fairness of users in recommender systems, but also achieve better overall recommendation performance. CCS CONCEPTS• Information systems → Recommender systems; • Computing methodologies → Artificial intelligence.

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Section: Fair Recommendationmentioning

confidence: 99%

User-oriented Fairness in Recommendation

Chen

et al. 2021

Proceedings of the Web Conference 2021

Self Cite

154

141

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“…Interpretability: The most common view of interpretability in RS is to increase the transparency of algorithms [14], [15], [40], [43], [164], which is especially important in health RS. Reliable explanations can greatly improve end-users' confidence in the recommendation results [126].…”

Section: Challengesmentioning

confidence: 99%

An Overview of Recommendation Techniques and Their Applications in Healthcare

Yue

Wang

Zhang

et al. 2021

IEEE/CAA J. Autom. Sinica

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With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, CF-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

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“…Wang et al [27] combined a gradient boosting decision tree with an attention mechanism to obtain an explainable recommendation system. In addition, the popular Knowledge Graph has been applied to various explainable recommendation systems [28]- [30].…”

Section: B Explainability Of Recommendation Systemsmentioning

confidence: 99%

Neural Explicit Factor Model Based on Item Features for Recommendation Systems

et al. 2021

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In recent years, recommendation systems based on collaborative filtering (CF) have achieved a high performance. Most of the existing recommendation systems use similarity measures to determine the suitability of items for users based on latent factor models (LFM). However, these recommendation systems reduce the explainability of recommendations and hide the reasons for recommending specific items. As a result, users tend to distrust the recommendation results. To address this problem, we propose the neural explicit factor model (NEFM). Based on the user-item rating matrix, we propose adding both user-feature attention matrix and an item-feature quality matrix to improve the explainability of user and item vectors. In addition, a feedforward neural network and a one-dimensional convolutional neural network extract features from user, item and the item-feature vector. Finally, a prediction layer performs the inner product of user data, item data, and item features. Experiments on the MovieLens and Yahoo Movies datasets validate the proposed model, and comparisons with similar recommendation models show the higher accuracy and explainability of our proposal.INDEX TERMS Recommender systems, latent factor models, convolutional neural networks, collaborative filtering.

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Fairness-Aware Explainable Recommendation over Knowledge Graphs

Cited by 152 publications

References 36 publications

User-oriented Fairness in Recommendation

User-oriented Fairness in Recommendation

An Overview of Recommendation Techniques and Their Applications in Healthcare

Neural Explicit Factor Model Based on Item Features for Recommendation Systems

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