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Found 45 papers, 17 papers with code
Causal Learning for Trustworthy Recommender Systems: A Survey
However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS).
Investigating Reproducibility in Deep Learning-Based Software Fault Prediction
In these cases, it therefore remains challenging to exactly reproduce the results in the current research literature.
Improving Sequential Recommendations with LLMs
We conduct extensive experiments on three datasets and explore a large variety of configurations, including different language models and baseline recommendation models, to obtain a comprehensive picture of the performance of each approach.
Performance Comparison of Session-based Recommendation Algorithms based on GNNs
In session-based recommendation settings, a recommender system has to base its suggestions on the user interactions that are ob served in an ongoing session.
Team-related Features in Code Review Prediction Models
In this paper, we propose the use of team-related features to improve the performance of predictions that are helpful to build code reviewer recommenders, with our target predictions being the identification of reviewers that would participate in a review and the provided amount of feedback.
Leveraging Large Language Models for Sequential Recommendation
Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches.
Economic Recommender Systems -- A Systematic Review
In this work, we survey the existing literature on what we call Economic Recommender Systems based on a systematic review approach that helped us identify 133 relevant papers.
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
Offline reinforcement learning empowers agents to glean insights from offline datasets and deploy learned policies in online settings.
A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
In this domain, recommender systems are for example tasked with providing personalized recommendations for transportation, accommodation, points-of-interest (POIs), etc.
A Survey on Popularity Bias in Recommender Systems
We furthermore critically discuss today's literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
Reinforcement learning-based recommender systems have recently gained popularity.
Semi-supervised Adversarial Learning for Complementary Item Recommendation
In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i. e., for items for which no co-purchase statistics yet exist.
Recommender Systems: A Primer
Personalized recommendations have become a common feature of modern online services, including most major e-commerce sites, media platforms and social networks.
Collaborative Image Understanding
A series of experiments on datasets from e-commerce and social media demonstrates that considering collaborative signals helps to significantly improve the performance of the main task of image classification by up to 9. 1%.
Multi-Objective Recommender Systems: Survey and Challenges
Traditionally, recommender systems research predominantly focuses on developing machine learning algorithms that aim to predict which content is relevant for individual users.
INFACT: An Online Human Evaluation Framework for Conversational Recommendation
Conversational recommender systems (CRS) are interactive agents that support their users in recommendation-related goals through multi-turn conversations.
Evaluating Conversational Recommender Systems: A Landscape of Research
Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way.
INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation
A recent example of such a dataset is INSPIRED, which consists of recommendation dialogs for sociable conversational recommendation, where items and entities were annotated using automatic keyword or pattern matching techniques.
Fairness in Recommender Systems: Research Landscape and Future Directions
In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past.
Conversational Recommendation: A Grand AI Challenge
Animated avatars, which look and talk like humans, are iconic visions of the future of AI-powered systems.
Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis
Our simulations show that a hybrid strategy that puts more weight on consumer utility but without ignoring profitability considerations leads to the highest cumulative profit in the long run.
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art
Moreover, we find that for none of the accuracy measurements any of the considered neural models led to the best performance.
Conversational Recommendation: Theoretical Model and Complexity Analysis
With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation.
Towards Retrieval-based Conversational Recommendation
One main challenge is that these generated responses both have to be appropriate for the given dialog context and must be grammatically and semantically correct.
Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation
Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing recommendations to users.
Digital Nudging with Recommender Systems: Survey and Future Directions
These systems thereby influence which information is easily accessible to us and thus affect our decision-making processes though the automated selection and ranking of the presented content.
Session-aware Recommendation: A Surprising Quest for the State-of-the-art
Recommender systems are designed to help users in situations of information overload.
A Black-Box Attack Model for Visually-Aware Recommender Systems
Due to the advances in deep learning, visually-aware recommender systems (RS) have recently attracted increased research interest.
Exploring Longitudinal Effects of Session-based Recommendations
Specifically, we analyze to what extent algorithms of different types may lead to concentration effects over time.
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems
In recent years, algorithm research in the area of recommender systems has shifted from matrix factorization techniques and their latent factor models to neural approaches.
Hybrid Session-based News Recommendation using Recurrent Neural Networks
We describe a hybrid meta-architecture -- the CHAMELEON -- for session-based news recommendation that is able to leverage a variety of information types using Recurrent Neural Networks.
A systematic review and taxonomy of explanations in decision support and recommender systems
With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems.
A Survey on Conversational Recommender Systems
Recommender systems are software applications that help users to find items of interest in situations of information overload.
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
In our analysis, we discuss common issues in today's research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation.
Empirical Analysis of Session-Based Recommendation Algorithms
However, previous research indicates that today's complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy.
Measuring the Business Value of Recommender Systems
Recommender Systems are nowadays successfully used by all major web sites (from e-commerce to social media) to filter content and make suggestions in a personalized way.
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems.
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems
A particular problem in that context is that online readers are often anonymous, which means that this personalization can only be based on the last few recorded interactions with the user, a setting named session-based recommendation.
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes.
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article.
Are Query-Based Ontology Debuggers Really Helping Knowledge Engineers?
Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success.
Evaluation of Session-based Recommendation Algorithms
In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session.
Sequence-Aware Recommender Systems
In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process.
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation
Deep learning methods have led to substantial progress in various application fields of AI, and in recent years a number of proposals were made to improve recommender systems with artificial neural networks.
Price and Profit Awareness in Recommender Systems
Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user.
