Context-aware recommender system using trust network

Yebdri, Zeyneb El; Benslimane, Sidi Mohamed; Lahfa, Fedoua; Barhamgi, Mahmoud; Benslimane, Djamal

doi:10.1007/s00607-020-00876-9

Cited by 12 publications

(2 citation statements)

References 38 publications

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“…This survey scrutinizes various methodologies, obstacles, and future trajectories in amalgamating differential privacy with collaborative filtering techniques.Moreover, a context-aware recommender system El Yebdri, Z. [16] harnesses trust networks to augment recommendation accuracy by considering contextual cues and integrating trust relationships among users. This system strives to furnish personalized and relevant recommendations that align with the diverse preferences and interests of users.…”

Section: Methodsmentioning

confidence: 99%

Privacy Preserving Recommender Systems

2024

IJFMR

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Privacy-preserving recommender systems are a growing area of research and development due to concerns about user privacy in digital environments. This review paper examines the existing methodologies and techniques used in designing and implementing these systems, focusing on their application in e-commerce, social media, and personalized content delivery platforms. The paper discusses the fundamental principles of privacy-preserving recommender systems and the motivations behind their need. The review also highlights the challenges and opportunities associated with existing privacy-preserving recommender systems, including scalability, efficiency, and usability. In this review, we focus on the challenges and opportunities that come with recommendation systems, and compare different systems to see how well they scale up, how fast they work, and how easy they are to use.

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Section: Methodsmentioning

confidence: 99%

Privacy Preserving Recommender Systems

2024

IJFMR

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“…Researchers have also used demographic knowledge [13,14] to achieve major breakthroughs, while some scholars used score ranking prediction methods to enhance recommendation performance such as [15][16][17], and others chose the genetic algorithm used in the prediction process to improve recommendation performance, such as [18,19]. Context as a dynamic description of an item and a user's situation affects the user's decision-making process; hence, it is essential for any recommendation system in a big data environment [20][21][22]. ese algorithms alleviate the problems caused by data sparsity to some extent, improve the accuracy of calculation similarity and recommendation quality with different methods, but the implementation of the algorithm depends on a large amount of user information and calculation, which can be due to high complexity and hard implementation.…”

Section: Introductionmentioning

confidence: 99%

Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

Chen

2021

Scientific Programming

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Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.

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