Equal experience fairness in recommender systems reduces bias.
problem Bias in data leads to unfair item recommendations.
method Introduces equal experience fairness notion and optimization framework.
result Mitigates unfairness in recommendations with minimal accuracy loss.
DeepFair improves fairness in recommender systems without sacrificing accuracy.
problem Lack of bias management in recommender systems leads to unfair recommendations for minority groups.
method Deep Learning based Collaborative Filtering algorithm that balances fairness and accuracy.
result It is possible to make fair recommendations without losing significant accuracy.
Proposes auditing for envy-freeness in recommender systems to assess individual preferences.
problem Auditing fairness in recommender systems for individual preferences.
method Formulates a pure exploration problem in multi-armed bandits, proposing a sample-efficient algorithm with theoretical guarantees.
result Algorithm ensures fairness without deteriorating user experience on real-world datasets.
Matrix completion has a long-time history of usage as the core technique of recommender systems. In particular, 1-bit matrix completion, which considers the prediction as a ``Recommended'' or ``Not Recommended'' question, has proved its significance and validity in the field. However, while customers and products aggre…
Develops a method to estimate uncertainty for group-level recommendations in matrix completion.
problem Uncertainty estimation for group-level recommendations in matrix completion.
method Structured conformal inference method combining any matrix completion algorithm.
result Stronger group-level guarantees through structured calibration.
KHGRec tackles noisy and incomplete KG-enhanced recommendations by modeling complex interactions.
problem Challenges in integrating KGs for accurate recommendations, especially in complex higher-order interactions and heterogeneous modalities.
method KHGRec uses a collaborative knowledge heterogeneous hypergraph (CKHG) to model group-wise interdependencies, employing two hypergraph encoders and attention mechanisms.
result KHGRec achieves an average 5.18% relative improvement over state-of-the-art baselines on four real-world datasets.
A new method for recommending groups and activities based on geo-social data.
problem Recommendation of groups and activities based on geo-social data with social constraints.
method Proposes an attentive geo-social group recommendation method with an attention mechanism and a spatial query algorithm.
result Significantly outperforms baseline methods in real-world datasets.
DeepUnHide uses deep learning to reveal hidden demographic features in recommender systems.
problem Extracting hidden demographic features from recommender systems factors.
method Gradient-based localization in deep learning for feature extraction.
result DeepUnHide outperforms state-of-the-art feature selection methods.
Proposes a new model for explainable recommendation systems.
problem Developing predictive models that provide explanations for item recommendations.
method Generalized Additive Models with Manifest and Latent Interactions (GAMMLI).
result Advantages in both predictive performance and explainability.
Network-based models predict user preferences for items like movies and research articles.
problem Filtering and delivering personalized advice for users with many available products.
method Network models based on group memberships, using Monte Carlo sampling and Expectation-Maximization methods.
result Network models outperform leading approaches for recommendation.
Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system. Identifying influential users and studying their impact on other users is an important problem because it provides…
In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with…
A new recommender system uses slates and Thompson Sampling to improve diversity and click rates.
problem Recommending relevant content to users in a dynamic environment.
method Variational Bayesian Recurrent Neural Net, Thompson Sampling, hierarchical prior, in-slate Thompson Sampling.
result Improved click rates and diversity in recommended slates.
CAFL breaks feedback loops in recommender systems using causal inference.
problem Feedback loops in recommender systems compromise recommendation quality and homogenize user behavior.
method Causal Adjustment for Feedback Loops (CAFL) algorithm that breaks feedback loops using causal inference.
result CAFL improves recommendation quality compared to prior correction methods.
Recommender systems have been widely adopted by electronic commerce and entertainment industries for individualized prediction and recommendation, which benefit consumers and improve business intelligence. In this article, we propose an innovative method, namely the recommendation engine of multilayers (REM), for tenso…
Enhanced recommender system using ensemble learning and graph embedding.
problem Challenges in selecting relevant data for users from large datasets.
method Group classification, ensemble learning, fuzzy rules, decision tree, graph embedding.
result High efficiency of the presented method on MovieLens datasets.
New method corrects bias in recommendation systems for diverse user groups.
problem Bias in recommendation systems due to MNAR data.
method Counterfactual Robust Risk Minimization (CRRM) framework.
result Empirical validation of CRRM's superiority in fairness and generalization.
Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
problem Recommender systems learn from user choices but can stall if users blindly follow recommendations.
method The recommender learns user knowledge by observing choices, abstaining from recommending a choice when multiple alternatives produce similar payoffs.
result Learning rate and social welfare improve when the recommender abstains from recommending certain choices.
Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user grou…
Machine learning models learn what we teach them to learn. Machine learning is at the heart of recommender systems. If a machine learning model is trained on biased data, the resulting recommender system may reflect the biases in its recommendations. Biases arise at different stages in a recommender system, from existi…
Develops a real-time exercise recommendation system using deep learning.
problem Improving accuracy in exercise recommendation systems without user feedback.
method Deep recurrent neural network with attention mechanisms, real-time expert feedback.
result Improved accuracy in exercise recommendation system after real-time active learning.
Survey of IoT recommendation systems and their limitations.
problem Traditional recommender systems fail to handle IoT data.
method Comprehensive review of IoT recommender systems and techniques.
result Proposes a reference framework for future research.
Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacke…
SharedMF uses secret sharing to protect privacy in distributed recommendation systems.
problem Privacy issues in multi-source data for recommendation systems.
method Federated learning and secret sharing technology.
result SharedMF achieves faster execution speed and better data adaptability compared to homomorphic encryption methods.
Improves recommender systems using mathematical principles.
problem Accuracy and speed of recommender systems.
method Explains and analyzes mathematical principles and algorithms.
result Describes the strengths and weaknesses of mathematical distance methods.
Data poisoning attacks can manipulate recommender systems to recommend target items.
problem Attacks on recommender systems to influence top-N item recommendations.
method Formulated as an optimization problem, solved using influence function to select influential users.
result Effective data poisoning attacks that outperform existing methods.
Survey on using knowledge graphs for better recommender systems.
problem Data sparsity and cold start issues in recommender systems.
method Utilizes knowledge graphs to improve recommendation accuracy and provide explanations.
result Advantages of knowledge graph-based recommender systems.
Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation b…
Proposes a deep hybrid model for better recommendation systems.
problem Limited studies on hybrid recommender systems and the need for more advanced approaches.
method Integrates deep learning with ID embeddings and auxiliary features for improved recommendation.
result Improves recommendation results over deep learning models using ID embeddings.
Deep learning improves conversational recommender systems.
problem Creating effective goal-oriented dialogue systems through natural language interactions.
method Apply deep learning techniques to conversational recommender systems.
result Deep learning models enhance user preference learning in CRS.
Data poisoning attacks can fool neighborhood-based recommender systems.
problem Robustness of neighborhood-based recommender systems to data poisoning attacks.
method Proposed a novel data poisoning attack framework (UNAttack) to fool the systems.
result Neighborhood-based recommender systems can be easily fooled by well-designed fake users.
The paper explores fairness in multi-component recommender systems.
problem How to ensure fairness in recommender systems composed of multiple models.
method Study of fairness ranking metrics, theoretical analysis, and empirical evaluation.
result Fairness in recommendation systems can be achieved by improving individual components.
Among the machine learning applications to business, recommender systems would take one of the top places when it comes to success and adoption. They help the user in accelerating the process of search while helping businesses maximize sales. Post phenomenal success in computer vision and speech recognition, deep learn…
The paper studies and mitigates bias in recommender systems.
problem Bias in recommender systems caused by user feedback loops.
method Modeling user exposure and proposing debiasing strategies.
result Recommender systems are biased and depend on user exposure.
New methods ensure fair rankings in web-scale recommender systems.
problem Ensuring fairness in recommender systems, especially in web-scale applications.
method Scalable methods for achieving fairness in rankings, addressing position bias.
result Our methods effectively achieve fairness in various recommender systems.
Interprets feature interactions in ad-click prediction models.
problem Improving interpretability of black-box recommender systems.
method Interprets feature interactions from a source model and encodes them in a target model.
result Interpretations significantly outperform existing recommender models.
Proposes HBGNN for better recommendation systems using graph neural networks.
problem Sparse structured data in recommendation systems lacking feature richness.
method Hierarchical BiGraph Neural Network (HBGNN) using bigraph framework.
result Competitive performance compared to current methods.
Confounding biases recommender systems, even when data seems fully observed.
problem Unmeasured features influencing both treatment and outcome in recommender systems.
method Illustrations and simulation studies showing how common practices introduce confounding.
result Standard recommender system practices can introduce confounding, reducing performance.
New method detects and measures malicious users in recommendation algorithms.
problem Identifying and quantifying malicious user activity in recommendation systems.
method Probabilistic programming for a disentangled model of malicious and regular user behavior.
result Simulation-based measure for quantifying malicious user effects.
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper…
Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding …
Adobe research tackles strategic recommendations using reinforcement learning.
problem Optimizing user interactions for long-term objectives in various use-cases.
method Reinforcement learning applied to modeling user behavior and decision-making.
result Practical solutions implemented for various use-cases, addressing fundamental challenges.
The increasing role of recommender systems in many aspects of society makes it essential to consider how such systems may impact social good. Various modifications to recommendation algorithms have been proposed to improve their performance for specific socially relevant measures. However, previous proposals are often …
We present a large scale hyperbolic recommender system. We discuss why hyperbolic geometry is a more suitable underlying geometry for many recommendation systems and cover the fundamental milestones and insights that we have gained from its development. In doing so, we demonstrate the viability of hyperbolic geometry f…
Incentive-aware recommender system for online platforms.
problem Myopic agents exploit optimal arms, not exploring alternatives.
method Model as multi-agent bandit problem, incentivizes exploration.
result Asymptotically optimal performance with ex-post fairness.
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper…
Auto-Surprise automates recommender system selection and optimization.
problem Finding the best algorithm and hyperparameters for recommender systems.
method Extends Surprise library with TPE optimization for algorithm selection and hyperparameter tuning.
result Significantly faster in finding optimal hyperparameters compared to grid search.
MRIF models dynamic user interests at multiple temporal-ranges.
problem Capturing dynamic and multi-resolution user interests in recommendation.
method Multi-resolution Interest Fusion (MRIF) model that considers both temporal-ranges and drifts in user interests.
result MRIF outperforms state-of-the-art recommendation methods consistently.