In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …

The importance of recommender systems is growing rapidly due to the exponential increase
in the volume of content generated daily. This surge in content presents unique challenges …

In an era where the number of choices is overwhelming on the internet, it is crucial to filter,
prioritize and deliver relevant information to a user. A recommender system addresses this …

This paper aims to address the challenge of sparse and missing data in recommendation
systems, a significant hurdle in the age of big data. Traditional imputation methods struggle …

Sensitive information in images is can be leaked during attacks, resulting in the malicious
acquisition of personal information. To improve the robustness of attacking defence for video …

Recommender systems recommend items to users that would suit the users' preferences.
Suggesting personalized items in the context of a group of users is a non-trivial task. The …

As one of the most representative recommendation solutions, traditional collaborative
filtering models typically have limitations in dealing with large-scale, sparse data to capture …

Conventional group recommender systems fail to take into account the impact of group
dynamics on group recommendations, such as the process of reconciling individual …

Abstract Locally Sensitive Hashing (LSH) algorithms are classical algorithms commonly
used on the c-Approximate Nearest Neighbor (c-ANN) search problem. When using …

The rapid growth of data volumes has led to information overload, which impedes informed
decision-making. To solve this problem, recommender systems have emerged that analyze …