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.

This paper proposes two frameworks namely Self-supervised Q-learning and Self-Supervised Actor-Critic and integrates the proposed frameworks with four state-of-the-art recommendation models, demonstrating the effectiveness of the approach on real-world datasets.

A comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of e-commerce recommender systems to identify the gaps and significant issues of the RSs’ traditional methods, which guide the researchers to do future work.

A systematic review of GLRS is provided, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations.

Today, exploiting sentiment analysis has become popular in designing recommender systems in various fields, including the restaurant and food area. However, most of the sentiment analysis-based restaurant recommender systems only use static information such as food quality, price, and service quality. The analysis of users’ opinions and the extraction of their food preferences lead to the provision of personalized recommendations, which is a research gap in literature; In this paper, a context-aware recommender system is proposed that extracts the food preferences of individuals from their com...

A survey on reinforcement learning based recommender systems (RLRSs) is presented and it is recognized and illustrated that RLRSs can be generally classified into RL- and DRL-based methods and proposed an RLRS framework with four components, i.e., state representation, policy optimization, reward formulation, and environment building.

This article provides a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks and systematically analyze the challenges of applying GNN on different types of data.

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in ...

The proposed system of IoT and ML is enabled for soil testing using the sensors, is based on measuring and observing soil parameters, which lowers the probability of soil degradation and helps maintain crop health.

Recommender systems have been widely applied to several domains and applications to assist decision making by recommending items tailored to user preferences. One of the popular recommendation algorithms is the model-based approach which optimizes a specific objective to improve the recommendation performance. These traditional recommendation models usually deal with a single objective, such as minimizing the prediction errors or maximizing the ranking quality of the recommendations. In recent years, there is an emerging demand for multi-objective recommender systems in which multiple objectiv...

This work proposes a multi-modal graph attention technique to conduct information propagation over MMKGs, and then uses the resulting aggregated embedding representation for recommendation.

This paper presents the task of topic-guided conversational recommendation, and proposes an effective approach to this task, and contributes a new CRS dataset named TG-ReDial (Recommendation through Topic-Guided Dialog), which has two major features.

To prove the effectiveness, K-NN algorithms and collaborative filtering are used to mainly focus on enhancing the accuracy of results as compared to content-based filtering, based on cosine similarity using k-nearest neighbor with the help of a collaborative filtering technique.

A systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc, provides a much-needed overview of the current state of research in this field.

This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR), and proposes an exclusive definition of SSR, on top of which a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid.

The first empirical evidence that MoRec is already comparable to its IDRec counterpart with an expensive end-to-end training method, even for warm item recommendation is provided.

An improved deep autoencoder model is developed, named Sparse Stacked Denoising AutoenCoder (SSDAE), to address the data sparse and imbalance problems for social networks and incorporates these deep representations and matrix factorization model into a uniform framework for recommender system.

An automated way of resume Classification and Matching could really ease the tedious process of fair screening and shortlisting, it would certainly expedite the candidate selection and decisionmaking process.

A novel universal sequence representation learning approach that utilizes the associated description text of items to learn transferable representations across different recommendation scenarios, and leads to a performance improvement in a cross-platform setting, showing the strong transferability of the proposed universal SRL method.

While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisf...

This research proposes a social-hybrid recommender system in the context of social commerce that recommends tourist attractions for each tourist based on the similarity of users' desires and interests, trust, reputation, relationships, and social communities.