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:10.48550/arxiv.2006.02046

Cited by 6 publications

(3 citation statements)

References 29 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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144

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

144

141

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“…However, the heterogeneity of data in existing matching systems poses great challenges for implicit matching algorithms when considering interpretability [172]. Some preliminary related works about interpretable matching algorithms can be found in [173], [174], [175].…”

Section: B Matching Principlesmentioning

confidence: 99%

Matching Algorithms: Fundamentals, Applications and Challenges

Ren

Xia

Chen

et al. 2021

IEEE Trans. Emerg. Top. Comput. Intell.

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Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the questionanswer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching.

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“…In [20], Rex et al proposed a data-efficient Graph Convolution Networks algorithms called PinSage, which combines efficient random walk and graph convolution to learn a representation of nodes. [21], [22], [23] are the other set of work on representation learning using Graph.…”

Section: Previous Workmentioning

confidence: 99%

Grouping Search Results with Product Graphs in E-commerce Platforms

Ranganath¹,

Das²,

Sanjay³

et al. 2021

Preprint

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Showing relevant search results to the user is the primary challenge for any search system. Walmart e-commerce provides an omnichannel search platform to its customers to search from millions of products. This search platform takes a textual query as input and shows relevant items from the catalog. One of the primary challenges is that this queries are complex to understand as it contains multiple intent in many cases. This paper proposes a framework to group search results into multiple ranked lists intending to provide better user intent. The framework is to create a product graph having relations between product entities and utilize it to group search results into a series of stacks where each stack provides a group of items based on a precise intent. As an example, for a query "milk, " the results can be grouped into multiple stacks of "white milk", "low-fat milk", "almond milk", "flavored milk". We measure the impact of our algorithm by evaluating how it improves the user experience both in terms of search quality relevance and user behavioral signals like Add-To-Cart.

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Fairness-Aware Explainable Recommendation over Knowledge Graphs

Cited by 6 publications

References 29 publications

User-oriented Fairness in Recommendation

User-oriented Fairness in Recommendation

Matching Algorithms: Fundamentals, Applications and Challenges

Grouping Search Results with Product Graphs in E-commerce Platforms

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