A Privacy-Preserving Multi-Task Framework for Knowledge Graph Enhanced Recommendation

Yu, Bin; Zhou, Chenyu; Zhang, Chen; Wang, Guodong; Fan, Yiming

doi:10.1109/access.2020.3004250

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…However, they also suffer privacy disclosure issues. Laplacian noise is added to have a privacy-preserving multitask framework (Yu et al , 2020). It is possible to attack using a malicious server and avail rating given by the user.…”

Section: Techniquesmentioning

confidence: 99%

Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda

Pramod

2022

DTA

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PurposeThis study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security.Design/methodology/approachThe study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review.FindingsIt is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions.Originality/valueThe study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.

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Section: Techniquesmentioning

confidence: 99%

Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda

Pramod

2022

DTA

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“…However, this research does not consider privacy protection. In light of this, Yu et al [49] employed the Laplacian noise to optimize recommendation process based on KG. However, the above literature only leveraged a single relationship to construct the KG framework, which is difficult to cover multiple relationships in practice.…”

Section: Knowledge-based Recommender Systemsmentioning

confidence: 99%

Trusted Service Evaluation for Mobile Edge Users: Challenges and Reviews

et al. 2021

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With the increasing growth of web services shared in various mobile edge platforms, it becomes necessary to evaluate all the candidates based on their quality of services to reduce the users’ service selection cost. However, the service quality data released by service providers cannot be simply deemed as trusted due to various subjective or objective reasons, which further produce a series of serious trust-aware service evaluation problems, including service quality data sparsity and lack of feedback incentive. In view of this, we summarize the challenging issues existing in the current research field of trusted mobile edge service evaluation. Afterward, we review the current research status of the trusted service evaluation in the mobile edge environment and discuss one of the typical application scenarios based on trusted service evaluation, that is, recommender systems, as well as their diverse categories. We believe this research could be helpful in assisting a mobile edge platform to build a trusted reputation system for various smart applications hosted in the mobile edge platform.

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“…Table 1: A brief comparison between user-item interactions, side information, and knowledge. Employing side information and knowledge as a supplement to user-item interactions can promote recommendation accuracy [59][60][61][62], providing richer user-item relations to promote the accuracy of inference for user preference and can substantially alleviates the sparsity and cold start problems. User-item interactions, the primary resources for the recommendation, generally contain two aspects: explicit and implicit ones divided according to whether the interactions explicitly carry the user's affection degree on items or not.…”

Section: Informationmentioning

confidence: 99%

“…In addition, controversies over graph embeddingbased recommendation and conventional recommendation are also embodied in recommendation accuracy. Although by employing side information and knowledge, graph embedding-based recommendation can achieve distinctive improvement in recommendation accuracy beyond conventional recommendation [59][60][61][62], while it seemly still reveals the relative weakness in some recommendation tasks for predicting implicit user-item interactions compared with conventional recommendation, proved in Sec. 6 on simulations.…”

Section: Introductionmentioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

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Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

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A Privacy-Preserving Multi-Task Framework for Knowledge Graph Enhanced Recommendation

Cited by 11 publications

References 34 publications

Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda

Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda

Trusted Service Evaluation for Mobile Edge Users: Challenges and Reviews

Recommender systems based on graph embedding techniques: A comprehensive review

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