Recommender systems used on e-commerce websites (e-commerce RS) are crucial to enhance the online shopping experiences of customers and improve business performance. Despite ongoing enhancements in their effectiveness, e-commerce RS face challenges that could harm customers, businesses, and society. Addressing these challenges effectively is crucial to protecting the interests of stakeholders relying on e-commerce RS. Proposed strategies include developing technical solutions, increasing customer awareness, and enacting relevant laws and regulations. However, current research has not yet thoroughly examined these solutions in a unified manner. This review fills that gap by providing a detailed overview of the hazards associated with e-commerce product recommendations and the measures implemented to mitigate these hazards. The paper assesses these solutions within the context of existing research, emphasizing their implications and suggesting future directions to improve the safety and efficacy of e-commerce RS.

AI-driven recommendation algorithms increasingly shape user experience on social media, raising concerns about transparency, accountability, and user agency. This paper presents a comparative analysis of Explainable AI (XAI) implementations on Facebook, Instagram, TikTok, and Twitter/X. Using a structured framework, we assess two key dimensions: Explanation Adequacy, defined by clarity, specificity, relevance, and verifiability, and Explanation Actionability, defined by proximity, granularity, and reversibility of controls. Our evaluation combines feature audits, cross-platform comparisons, and rubric-based scoring. Results show Facebook provides the strongest balance of adequacy (4/5) and actionability (4/5), Twitter/X offers limited adequacy (2/5) but moderate actionability (3/5), TikTok demonstrates strong actionability (4/5) but generic explanations, and Instagram performs moderately ($3 / 5$on both dimensions). We further extend the analysis by linking adequacy and actionability to perceived usefulness, trust, satisfaction, and algorithmic scepticism. Findings highlight tensions between algorithmic sophistication, user comprehension, and engagement optimization, while also revealing regulatory implications under GDPR and the DSA. This work contributes a standardized evaluation framework for XAI in social computing, empirical evidence of platform disparities, and practical design guidelines for enhancing transparency and user empowerment in recommender systems.

The explosive development of the metaverse as a socio-technical system is a major challenge to national security and governance. The current paper analyses the existing academic and industry literature in order to determine the key risks and its implications. We find that the metaverse is bringing in novel vectors of misinformation and disinformation, radicalisation, financial crime, terrorism, and state-sponsored hybrid warfare, whereas among the most specific dangers are the recruitment of extremists in the virtual realm of immersion, laundering of illicit funds through decentralised economies, identity theft, and critical infrastructure exploitation. The majority of the existing mitigation measures suggested include cybersecurity tools, identity verification, content moderation, and regulatory measures but according to our analysis, coordinated governance, interdisciplinary research, and adaptable policy frameworks are required. The paper offers an in-depth insight into ways of reducing metaverse-associated threats to national security through the combination of technical, social, and policy approaches.

Data analysis requires a pairwise proximity measure over objects. Recent work has extended this to situations where the distance information between objects is given as comparison results of distances between three objects (triplets). Humans find comparison tasks much easier than the exact distance computation, and such data can be easily obtained in big quantities via crowdsourcing. In this work, we propose triplets augmentation, an efficient method to extend the triplets data by inferring the hidden implicit information from the existing data. Triplets augmentation improves the quality of kernel-based and kernel-free data analytics. We also propose a novel set of algorithms for common data analysis tasks based on triplets. These methods work directly with triplets and avoid kernel evaluations, thus are scalable to big data. We demonstrate that our methods outperform the current best-known techniques and are robust to noisy data.

Analysis of the fairness of machine learning (ML) algorithms has attracted many researchers’ interest. Several studies have shown that ML methods produce a bias toward different groups, which limits the applicability of ML models in many applications, such as crime rate prediction. The data used for ML may have missing values, which, if not appropriately handled, are known to further harmfully affect fairness. To address this issue, many imputation methods have been proposed to deal with missing data. However, research on the effect of missing data imputation on fairness is still rather limited. In this paper, we analyze the impact of imputation on fairness in the context of graph data (node attributes) using different embedding and neural network methods. Extensive experiments on six datasets demonstrate several issues of fairness in graph node classification when dealing with missing data and various imputation techniques. We find that the choice of the imputation method affects both fairness and accuracy. Our results provide valuable insights into fairness ML over graph data and how to handle missingness in graphs efficiently.

Recommender systems using Collaborative Filtering techniques are capable of make personalized predictions. However, these systems are highly vulnerable to profile injection attacks. Group attacks are attacks that target a group of items instead of one, and there are common attributes among these items. Such profiles will have a good probability of being similar to a large number of user profiles, making them hard to detect. We propose a novel technique for identifying group attack profiles which uses an improved metric based on Degree of Similarity with Top Neighbors (DegSim) and Rating Deviation from Mean Agreement (RDMA). We also extend our work with a detailed analysis of target item rating patterns. Experiments show that the combined methods can improve detection rates in user-based recommender systems.

Our work focuses on tackling the problem of efficiency and accuracy of web usage clustering for recommender systems. Accurate analysis and preprocessing of web usage data and efficient web usage clustering are the key factors that influence the development of clustering based implicit recommender system. We propose an analysis and preprocessing model to tackle the poor quality of web usage data. To address the problem of efficient web usage clustering, we propose a Particle Swarm Optimization (PSO) based clustering approach. Having shown our PSO based clustering performs well; we extend it for mining the usage behavior of web users. We select Java API (Application Programming Interface) documentation usage data as a case study for our recommender system.

To fulfill the increasing demand of business for the latest information, current data integration approaches are moving towards real-time updates. One important element in real-time data integration is the join of a continuous incoming data stream with a disk-based relation. In this paper we investigate a stream-based join algorithm, called mesh join (MESHJOIN), and propose an improved version called reduced MESHJOIN (R-MESHJOIN). Both algorithms tune the memory, allocating parts of the memory to key components. In MESHJOIN there is a dependency between the size of partitions in an internal queue for the stream data and the number of iterations required to bring the disk-based relation into memory. This dependency hampers the optimal distribution of memory among the join components. In particular the size of the disk-buffer varies with the size of the disk-based relation which is unnecessary. On the other hand the R-MESHJOIN algorithm removes this dependency. This enables an optimal distribution of available memory among the join components. In R-MESHJOIN a change in the size of the disk-based relation does not affect the size of the disk-buffer. An experimental study is conducted in order to validate the arguments.