Science topic
Recommender Systems - Science topic
Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item (such as music, books, or movies) or social element (e.g. people or groups) they had not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user's social environment (collaborative filtering approaches)
Publications related to Recommender Systems (10,000)
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The constant advancement of web development trends and technology has resulted in a large number of web systems that are frequently visited on a regular basis. Among the web systems that have been established include systems that allow users to listen to music online without having to download it to their devices. With the increasing popularity of...
This intermediate-level tutorial, titled "Gen-RecSys," merges both industrial and academic perspectives on recent advances in Generative AI for recommender systems (beyond LLMs). It aims to highlight the transformative role of generative models in modern recommender systems, which have significantly impacted the AI field-particularly with the rise...
The exponentially scaling domain of a smart learning frameworks necessitates advanced recommender systems capable of providing precise and relevant educational content to learners. Traditional recommender systems often grapple with challenges such as data sparsity, cold start problems, and the inability to capture complex user-item interactions & s...
E-commerce systems aim to deliver products on time and at a competitive price to customers through the Internet. Though transformational, adapting to an internet-based online system led to higher shipping charges (being borne by customers) and overwhelming options (requiring endless time to make purchasing decisions). Most existing shipping policie...
The ability to automate and personalize the recommendation of multimedia contents to consumers has been gaining significant attention recently. The burgeoning demand for digitization and automation of formerly analog communication processes has caught the attention of researchers and professionals alike. In light of the recent interest and anticipa...
In recent years, the proliferation of options available to users has made it increasingly challenging for individuals to identify and select items that align with their interests. This abundance of information has similarly posed significant difficulties for organizations tasked with extracting meaningful insights and providing relevant recommendat...
We propose FACTER, a fairness-aware framework for LLM-based recommendation systems that integrates conformal prediction with dynamic prompt engineering. By introducing an adaptive semantic variance threshold and a violation-triggered mechanism, FACTER automatically tightens fairness constraints whenever biased patterns emerge. We further develop an...
Shilling and adversarial attacks are two main types of attacks against recommender systems (RSs). In modern RSs, existing defense methods are hindered by the following two challenges: (1) the diversity of RSs’ information sources beyond the interaction matrix, such as user comments, textual data, and visual information; and (2) most defense methods...
Graph Neural Networks (GNNs) have significantly advanced recommendation systems by modeling user-item interactions through bipartite graphs. However, real-world user-item interaction data are often sparse and noisy. Traditional bipartite graph modeling fails to capture higher-order relationships between users and items, limiting the ability of GNNs...
There are several areas where self-learning AI is actively used. Machine learning and deep learning allow you to identify patterns and improve performance. Algorithms such as neural networks can adapt and improve based on experience. Self-learning GPTs are used to dialogue with humans. Computer vision recognizes and classifies images. Recommender s...
Selecting crossing combinations crucial for successfully developing new improved crop varieties and genomic data from DNA markers have become invaluable for guiding plant breeders in evaluating and choosing promising crosses between potential parents. However, navigating the vast array of thousands of possible parental combinations, even with exten...
This research introduces a novel recommender system for adapting single-machine problems to distributed systems within the MapReduce (MR) framework, integrating knowledge and text-based approaches. Categorizing common problems by five MR categories, the study develops and tests a tutorial with promising results. Expanding the dataset, machine learn...
In recommender systems, leveraging auxiliary behaviors (e.g. view, cart) to enhance the recommendation in the target behavior (e.g. purchase) is crucial for mitigating the sparsity issue inherent in single-behavior recommendation. This has given rise to the multi-behavior recommendation (MBR). Existing MBR task faces two primary challenges. First,...
The development of recommendation systems represents a critical challenge in machine learning, particularly in today’s digital landscape. These systems play a pivotal role in delivering personalized product and service suggestions tailored to user preferences, thereby enhancing user experience and driving sales revenue. While methodologies such as...
Diffusion-based recommender systems (DR) have gained increasing attention for their advanced generative and denoising capabilities. However, existing DR face two central limitations: (i) a trade-off between enhancing generative capacity via noise injection and retaining the loss of personalized information. (ii) the underutilization of rich item-si...
Context-aware social media recommendation has been important in many applications such as e-commerce and entertainment. However , existing systems consider pre-specified contexts and cannot well handle user preferences, which negatively affects the recommendation quality and efficiency, and causes them not extendable to various applications. In thi...
Model-driven engineering (MDE) has seen significant advancements with the integration of machine learning (ML) and deep learning techniques. Building upon the groundwork of previous investigations, our study provides a concise overview of current large language models (LLMs) applications in MDE, emphasizing their role in automating tasks like model...
Recommender systems typically generate predictions by analyzing interaction graphs that connect users and items. Many state-of-the-art approaches leverage graph neural networks (GNNs) to learn meaningful representations , assuming homophily-where similar users are positioned closely in the graph. However, recent studies indicate that real-world rec...
Federated recommender systems enhance privacy by allowing clients to train local models and share only updates with a central server. However, removing specific client contributions (unlearning) remains a challenge, as existing methods are designed for centralized settings. We propose CFRU, a novel federated recommendation unlearning framework that...
Nostalgic comments about the early internet often praise its random, chaotic aesthetic. By contrast, the major platforms of today are typically viewed as corporate in aesthetic, with a one-size-fits-all profile and personalised recommendations of products. The curated life is the opposite of the serendipitous life. Instead of seeing strange or unus...
Artificial intelligence (AI) has emerged as a disruptive force in today's quickly expanding digital economy, where online firms are always exploring novel tactics to engage and keep clients. Among its numerous uses, AI-driven customization tactics stand out, altering how e-commerce and digital marketing interact with customers [1]. The integration...
Given the exponential advancement in AI technologies and the potential escalation of harmful effects from recommendation systems, it is crucial to simulate and evaluate these effects early on. Doing so can help prevent possible damage to both societies and technology companies. This paper introduces the Recommender Systems LLMs Playground (RecSysLL...
Recommender systems often rely on graph-based filters, such as normalized item-item adjacency matrices and low-pass filters. While effective, the centralized computation of these components raises concerns about privacy, security, and the ethical use of user data. This work proposes two decentralized frameworks for securely computing these critical...
Recommender systems rely on Collaborative Filtering (CF) to predict user preferences by leveraging patterns in historical user-item interactions. While traditional CF methods primarily focus on learning compact vector embeddings for users and items, graph neural network (GNN)-based approaches have emerged as a powerful alternative, utilizing the st...
Recent advances in recommender systems have shown that user-system interaction essentially formulates long-term optimization problems, and online reinforcement learning can be adopted to improve recommendation performance. The general solution framework incorporates a value function that estimates the user's expected cumulative rewards in the futur...
Artificial intelligence (AI) models are increasingly autonomous in decision-making, making pursuing responsible AI more critical than ever. Responsible AI (RAI) is defined by its commitment to transparency, privacy, safety, inclusiveness, and fairness. But while the principles of RAI are clear and shared, RAI practices and auditing mechanisms are s...
Recommendation algorithms based on graph convolutional networks can integrate user and item node information along with their interaction topology, better capturing the intricate relationships between users and items, thereby enhancing the accuracy of recommender systems. However, existing methods often overlook the impact of noise in user behavior...
Recommender systems are pivotal in delivering personalised user experiences across various domains. However, capturing the heterophily patterns and the multi-dimensional nature of user-item interactions poses significant challenges. To address this, we introduce FWHDNN (Fusion-based Wavelet Hypergraph Diffusion Neural Networks), an innovative frame...
In the ever-changing field of higher education, students continually face challenges in optimizing their academic paths by selecting courses that match their interests and career goals. To address this, we propose an Add-On Course Recommendation System designed to integrate seamlessly with existing educational frameworks. In today's dynamic educati...
Collaborative Filtering (CF) plays a pivotal role in recommendation systems by predicting user preferences through the analysis of behaviors and preferences of similar users. Traditional CF approaches have been successful by representing users and items in low-dimensional spaces derived from historical interaction data. However, more recent advance...
Educational recommender systems (ERSs) play a crucial role in personalizing learning experiences and enhancing educational outcomes by providing recommendations of personalized resources and activities to learners, tailored to their individual learning needs. However, their effectiveness is often diminished by insufficient user control and limited...
The click-through rate (CTR) forecast is among the mainstream research directions in the domain of recommender systems, especially in online advertising suggestions. Among them, the multilayer perceptron (MLP) has been extensively utilized as the cornerstone of deep CTR prediction models. However, current neural network-based CTR prediction models...
Background
Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous fashion.
Results
We introduce the novel Joint Embedding Learning-classifier for improved Interpretabi...
Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and items into a shared latent space, but the sparsity of interactions often poses challenges. While leveraging user reviews could mitigate this sparsity, existing review-awa...
Multimodal music information retrieval (MIR) has gained much significance and there exists a plethora of datasets in different formats as well as machine learning and deep learning models built on these datasets for MIR tasks. However, these datasets are mostly found in high-resourced languages making the models biased to these cultures and languag...
Climate change is among the most critical global challenges, but international policies to meet the Paris Agreement targets remain inadequate. Despite a strong scientific consensus on climate change and its causes, a "consensus gap" persists between scientists and the public, especially regarding mitigation efforts. This thesis investigates factors...
Keeping ML-based recommender models up-to-date as data drifts and evolves is essential to maintain accuracy. As a result, online data preprocessing plays an increasingly important role in serving recommender systems. Existing solutions employ multiple CPU workers to saturate the input bandwidth of a single training node. Such an approach results in...
The use of AI-driven personalisation methods has become a revolutionary force in the quickly changing world of digital marketing and e-commerce. This chapter offers a thorough analysis of how AI-driven personalisation methods are revolutionising digital client targeting. To set the context for the significant influence, an overview of the importanc...
This study presents Poison-RAG, a framework for adversarial data poisoning attacks targeting retrieval-augmented generation (RAG)-based recommender systems. Poison-RAG manipulates item metadata, such as tags and descriptions, to influence recommendation outcomes. Using item metadata generated through a large language model (LLM) and embeddings deri...
Neural news recommender systems (RSs) have integrated language models (LMs) to encode news articles with rich textual information into representations, thereby improving the recommendation process. Most studies suggest that (i) news RSs achieve better performance with larger pre-trained language models (PLMs) than shallow language models (SLMs), an...
Recommendation systems are crucial in managing data overload, enabling online platforms to provide users with personalized recommendations. However, these systems often encounter significant challenges, such as the cold start problem and data sparsity, which hinder recommendation accuracy. To address these issues effectively, this study proposes an...
Recommender systems play a pivotal role in guiding user decisions across diverse domains, such as e-commerce, entertainment, and education. However, the opaque nature of many recommendation algorithms often undermines user trust and engagement. This study explores the potential of human-AI collaboration to enhance the explainability of recommender...
In recommendation tasks, most existing models that learn users’ preferences from user–item interactions ignore the relationships between items. Additionally, ensuring that the crossed features capture both global graph structures and local context is non-trivial, requiring innovative techniques for multi-scale representation learning. To overcome t...
Collaborative recommendation systems have been widely used in various fields, such as movies, music and e-commerce. However, due to the natural openness of its ratings, it is vulnerable to shilling attacks. Shilling attacks greatly affect the accuracy and trustworthiness of recommendation systems, so we urgently need effective methods to counter sh...
Lately, we have observed a growing interest in intent-aware recommender systems (IARS). The promise of such systems is that they are capable of generating better recommendations by predicting and considering the underlying motivations and short-term goals of consumers. From a technical perspective, various sophisticated neural models were recently...
Recommender systems remain an essential topic due to its wide application in various domains and the business potential behind them. With the rise of deep learning, common solutions have leveraged neural networks to facilitate collaborative filtering, and some have turned to generative adversarial networks to augment the dataset and tackle the data...
Deep learning and machine learning techniques in market analysis have gained tremendous popularity because of its" learning feature." These techniques are applied in various ways within business organizations to handle tasks such as prediction, feature extraction, natural language processing and recommendation etc. In the domain of recommendation s...
Comparative recommendation explanations help to make sense of recommendations by comparing a recommended item along some aspects of interest with one or many items being considered. This work extends the notion of comparative explanations, by going beyond merely better/worse statements, to further incorporate aspect-level opinions for more informat...
Conversational recommender systems (CRS) involve both recommendation and dialogue tasks, which makes their evaluation a unique challenge. Although past research has analyzed various factors that may affect user satisfaction with CRS interactions from the perspective of user studies, few evaluation metrics for CRS have been proposed. Recent studies...
Knowledge-aware recommendation systems often face challenges owing to sparse supervision signals and redundant entity relations, which can diminish the advantages of utilizing knowledge graphs for enhancing recommendation performance. To tackle these challenges, we propose a novel recommendation model named Dual-Intent-View Contrastive Learning net...
This paper demonstrates the successful application of Off-Policy Evaluation (OPE) to accelerate recommender system development and optimization at Adyen, a global leader in financial payment processing. Facing the limitations of traditional A/B testing, which proved slow, costly, and often inconclusive, we integrated OPE to enable rapid evaluation...
IIR 2024, the 14th Italian Information Retrieval Workshop, served as the annual event for the Information Retrieval (IR) and Recommender Systems (RS) communities both in Italy and collaborating with Italian research institutions. This year's event spanned two days and featured studies on various topics within IR, RS, and Large Language Models (LLMs...
Tourism is a significant source of income for countries and regions, and with the advancement of technology, everything is now interconnected, generating massive amounts of data. Recommender systems are one way to utilize this generated big data. However, currently, Ethiopian Tourism Institutions do not have a system to manage tourist sites, analyz...
Today e-commerce has had tremendous growth in the past years primarily due to changes in technology and customer’s buying behavior. One of the big shifts in the process has been the use of ML in advertising which has the capability to transform the marketing domain together with consumer interactions. This paper discusses the viability of using mac...
Link prediction is a fundamental problem in graph theory with diverse applications, including recommender systems, community detection, and identifying spurious connections. While feature-based methods achieve high accuracy, their reliance on node attributes limits their applicability in featureless graphs. For such graphs, structure-based approach...
The increasing integration of Artificial Intelligence (AI) in recommendation systems has raised concerns regarding the transparency and interpretability of AI-driven decisions. While explainable AI (XAI) techniques aim to address these challenges, ensuring user-centric explanations remains a critical factor in fostering trust and improving system u...
This work examines the role of recommender systems in promoting sustainability, social responsibility, and accountability, with a focus on alignment with the United Nations Sustainable Development Goals (SDGs). As recommender systems become increasingly integrated into daily interactions, they must go beyond personalization to support responsible c...
This survey is intended to inform non-expert readers about the field of recommender systems, particularly collaborative filtering, through the lens of the impactful Netflix Prize competition. Readers will quickly be brought up to speed on pivotal recommender systems advances through the Netflix Prize, informing their prospective state-of-the-art re...
Conversational recommender systems (CRSs) have garnered increasing attention for their ability to provide personalized recommendations through natural language interactions. Although large language models (LLMs) have shown potential in recommendation systems owing to their superior language understanding and reasoning capabilities, extracting and u...
Approximate Nearest Neighbors (ANN) search is a crucial task in several applications like recommender systems and information retrieval. Current state-of-the-art ANN libraries, although being performance-oriented, often lack modularity and ease of use. This translates into them not being fully suitable for easy prototyping and testing of research i...
Background
Rich data on diverse patients and their treatments and outcomes within Electronic Health Record (EHR) systems can be used to generate real world evidence. A health recommender system (HRS) framework can be applied to a decision support system application to generate data summaries for similar patients during the clinical encounter to ass...
Artificial Intelligence (AI) is utilized by the Product Recommender System (PRS) to learn the heterogeneous data for proper recommendation. None of the prevailing works focused on PRS centered on User Preference Data (UPD) and trends. Hence, a Swish Scaled Stochastic Depth Lasso-based Gated Recurrent Unit (3SDL-GRU)-based PRS is proposed in this pa...
Introduction
In recent years, the increased demand for food has prompted farmers to increase production to support economic expansion. However, the excessive use of mineral fertilizers poses a significant threat to the sustainability of food systems. In Colombia, coffee cultivation plays a fundamental role in the economy, thus creating a recognized...