Top Research Papers on Recommender Systems

A monolithic hybrid recommender system called Predictory is proposed, which combines a recommender module composed of a collaborative filtering system (using the SVD algorithm), a content-based system, and a fuzzy expert system that serves to recommend suitable movies.

The main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.

A novel music recommendation technique based on the identification of personality traits, moods, and emotions of a single user, starting from solid psychological observations recognized by the analysis of user behavior within a social environment is described.

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review, and identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact.

A detailed survey of existing approaches to conversational recommendation is provided, categorizing these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background.

The goal of this survey is to present recent advances on adversarial machine learning (AML) for the security of RS and to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions.

The paper carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning.

A taxonomy of smart city features, dimensions, actions and goals, and, according to these variables, the existing literature on recommender systems is surveyed, to show current opportunities and challenges where personalized recommendations could be exploited as solutions for citizens, firms and public administrations.

This survey reviews over 60 papers published in top conferences/journals and provides an elaborate taxonomy of fairness methods in the recommendation, and outlines some promising future directions on fairness in recommendation.

This work develops an algorithm that leverages classical recommendation models for causal recommendation and demonstrates that the proposed algorithm is more robust to unobserved confounders and improves recommendation.

The FEVR framework provides a structured foundation to adopt adequate evaluation configurations that encompass this required multi-facetedness and provides the basis to advance in the field.

Recommender systems aim at providing users with a list of recommendations of items that a service offers, for example, a video streaming service will typically rely on a recommender system to propose a personalized list of movies or series to each of its users.

This tutorial focuses on the critical challenges of GNN-based recommendation and the potential solutions, and discusses how to address these challenges by elaborating on the recent advances of GMM models with a systematic taxonomy from four critical perspectives.

A systematic and comprehensive review on SBRS is provided and a hierarchical framework is created to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities.

This review of HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations derived five reporting guidelines that can serve as a reference frame for future HRS studies.

This research re-formulates the aforementioned three recommendation policies into prompt formats tailored specifically to the domain at hand, and indicates that ChatGPT achieves an optimal balance between cost and performance when equipped with list-wise ranking.

A thorough review of the state-of-the-art of recommender systems that leverage multimedia content is presented, by classifying the reviewed papers with respect to their media type, the techniques employed to extract and represent their content features, and the recommendation algorithm.

Research about recommender systems emerges over the last decade and comprises\nvaluable services to increase different companies' revenue. Several approaches\nexist in handling paper recommender systems. While most existing recommender\nsystems rely either on a content-based approach or a collaborative approach,\nthere are hybrid approaches that can improve recommendation accuracy using a\ncombination of both approaches. Even though many algorithms are proposed using\nsuch methods, it is still necessary for further improvement. In this paper, we\npropose a recommender system method using a gra...

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also pr...

SHT, a novel Self-Supervised Hypergraph Transformer framework (SHT) which augments user representations by exploring the global collaborative relationships in an explicit way, is proposed for data augmentation over the user-item interaction graph, so as to enhance the robustness of recommender systems.