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.
A graph neural network detects beneficial feature interactions for recommender systems.
problem Feature interactions are crucial but not all are beneficial for recommendation accuracy.
method Graph neural network with L0 activation regularization for edge prediction.
result The model outperforms baselines and automatically identifies beneficial feature interactions.
Jointly optimizes tree index and deep model for better recommendation accuracy.
problem Improving recommendation accuracy in large-scale recommender systems.
method Develops a joint optimization framework for tree index and user preference model.
result Significantly improves recommendation accuracy on real-world datasets.
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.
A new method improves recommendation accuracy by learning from multiple networks and time-dependent user preferences.
problem Incomplete user profiles and dynamic user preferences degrade recommender quality.
method A cross-network time-aware recommender that learns from multiple source networks and develops current user models.
result The proposed solution achieves superior performance in accuracy, novelty, and diversity.
This paper improves fairness in recommendation systems by learning individual preferences across multiple dimensions.
problem Fairness in recommender systems, especially in areas with social impact.
method Opportunistic multi-aspect re-ranking approach that learns individual preferences and enhances provider fairness.
result Achieves a better trade-off between accuracy and fairness across multiple fairness dimensions.
FlexEncoder analyzes DAE parameters' impact on recommender systems.
problem Varying parameter settings lead to different performance in DAE-based recommender systems.
method Built FlexEncoder with configurable parameters to analyze parameter influences.
result DAE parameters significantly affect prediction accuracy across datasets.
New feature mapping approach improves recommendation accuracy and explainability.
problem Balancing recommendation accuracy and explainability using metadata.
method Maps uninterpretable features to interpretable aspect features, minimizing both prediction and interpretation losses.
result Strong performance in recommendation and explainability, eliminating metadata need.
The importance of accurate recommender systems has been widely recognized by academia and industry. However, the recommendation quality is still rather low. Recently, a linear sparse and low-rank representation of the user-item matrix has been applied to produce Top-N recommendations. This approach uses the nuclear nor…
The paper uses MRFs to improve recommendation accuracy in collaborative filtering.
problem Improving recommendation accuracy in collaborative filtering.
method Modeling dependencies via Gaussian Markov Random Fields (MRFs) with auto-normal parameterization and pseudo-likelihood.
result The proposed approach achieved competitive ranking-accuracy and a 20% gain in accuracy on the largest data-set.
Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-booste…
A hybrid approach uses RNNs to recommend news articles based on context and session history.
problem Challenging news recommendation due to varying user interests and factors.
method Context-aware, hybrid, deep learning approach using RNNs with additional information types.
result Significantly higher recommendation accuracy and catalog coverage compared to other session-based algorithms.
Matrix factorization was used to generate investment recommendations for investors. An iterative conjugate gradient method was used to optimize the regularized squared-error loss function. The number of latent factors, number of iterations, and regularization values were explored. Overfitting can be addressed by either…
Proposes a new model for diversified interactive recommendation.
problem Previous methods focus on optimizing recommendation accuracy, ignoring diversity.
method DC2B model using determinantal point process and Thompson sampling-based variational Bayesian inference. result Demonstrates effectiveness of the proposed method through extensive experiments.
Paper proposes a two-stage ranking for personalized TV recommendations.
problem Improving TV recommendation accuracy and efficiency.
method First, identifies potential candidates using user viewing patterns. Then, ranks them based on user preferences and program textual information.
result The proposed model outperforms in recommendation accuracy and efficiency.
Proposes a model encoder to recommend deep learning architectures for unknown datasets.
problem Choosing an appropriate deep learning architecture for unknown datasets is time-consuming and laborious.
method Proposes a model encoder approach to learn fixed-length representations of architectures and hyperparameters in an unsupervised manner.
result Predicted accuracy of recommended architectures is a good estimator of actual accuracy on unknown datasets.
Proposes intelligent web browser features for link prediction and category-wise recommendation.
problem Improving web browser intelligence for better link prediction and recommendation.
method Proposes frecency prediction for link prediction and URL classification for recommendation, with hyperparameter optimization.
result Improves browser recommendation accuracy by 10% through hyperparameter optimization.
Adaptive time decay functions improve financial product recommendation accuracy.
problem Inaccurate recommendations due to static historical data in finance.
method Time-dependent collaborative filtering with personalized decay functions.
result Significant improvements over state-of-the-art benchmarks in financial product recommendation.
We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of "hiding in the crowd" privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or nym) setting and the …
PMD measures user distances using optimal transportation, improving recommendation accuracy.
problem Sparse data and lack of co-rated items in collaborative filtering.
method PMD is an Earth Mover's Distance-based user distance measure that utilizes all ratings.
result PMD achieves superior recommendation accuracy, especially in sparse data scenarios.
This paper improves context-aware recommender systems by selecting and incorporating relevant low-dimensional contextual information.
problem Generating accurate recommendations is not enough; contextual information can cause issues like battery drain and privacy.
method Developed a feature-selection algorithm based on genetic algorithms to reduce context dimensions while maintaining explainability.
result The approach improves accuracy and transparency in recommendations, outperforming state-of-the-art models.
The profusion of online news articles makes it difficult to find interesting articles, a problem that can be assuaged by using a recommender system to bring the most relevant news stories to readers. However, news recommendation is challenging because the most relevant articles are often new content seen by few users. …
New algorithm uses negative user preferences to improve recommendation quality.
problem Insufficient utilization of negative user preferences in recommender systems.
method Explicitly models negative user preferences to recommend more positive items.
result Improves recommendation quality by increasing accuracy and reducing negative items.
In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i.e., number of seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the prevalence of the task of recommending items under capacity constraints in a variety of s…
SPMF improves social recommendation by considering trust and preference domains.
problem Ignoring trust and preference domain differences in social recommendations.
method SPMF uses matrix factorization with trust and preference segmentation.
result SPMF outperforms state-of-the-art recommendation algorithms.
In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the geometry of the user space to create meaningful clusters in the user domain. This reduces the dimensionality of the recommendation problem w…
CHAMELEON uses RNNs to recommend news sequences better than other methods.
problem Improving news recommendation accuracy and catalog coverage.
method Hybrid meta-architecture CHAMELEON with RNNs for sequence modeling and side information.
result Significantly higher recommendation accuracy and catalog coverage.
NAS model improves social recommendation accuracy using neural attention.
problem Capturing and weighing friends' preferences in social recommendation systems.
method Proposes a Neural Attention mechanism (NAS) for Social collaborative filtering.
result NAS model outperforms state-of-the-art methods in publicly available datasets.
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of the conditions under which item-based recommendations will succeed or fail. We investigate the existence of an ideal item-based CF method a…
KGRL uses reinforcement learning with knowledge graphs for better interactive recommendation.
problem Achieving responsiveness and accuracy in dynamic user-item interactions.
method KGRL combines reinforcement learning and knowledge graphs, using a local knowledge network and attention mechanism.
result KGRL outperforms state-of-the-art methods in simulated and real-world environments.
Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. T…
Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. This gap bet…
ComiRec framework predicts user interests for personalized recommendations.
problem Predicting user interests from sequential behavior data.
method ComiRec framework captures multiple user interests and balances recommendation accuracy and diversity.
result ComiRec achieves significant improvements over state-of-the-art models in sequential recommendation.
New methods improve recommendation accuracy for users and items with few ratings.
problem Skewed distribution and low ratings affect recommendation accuracy.
method Four matrix completion-based approaches: FARP, TMF, TMF + Dropout, IFWMF.
result Improved prediction accuracy for users and items with few ratings.
Recommender systems play an essential role in the modern business world. They recommend favorable items like books, movies, and search queries to users based on their past preferences. Applying similar ideas and techniques to Monte Carlo simulations of physical systems boosts their efficiency without sacrificing accura…
A deep learning strategy improves recommendation accuracy by leveraging trust and distrust relationships.
problem Data scarcity and cold-start problem in recommender systems.
method Social deep pairwise learning with a ranking loss function and social negative sampling.
result The proposed model achieves an 11.49% improvement over state-of-the-art methods.
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.
This paper reviews PU learning evaluation methods and provides practical recommendations.
problem Evaluating PU learning methods when only positive and unlabelled data are available.
method Critical review of 51 articles proposing PU classifiers and alternative predictive accuracy measures.
result Practical recommendations for improving PU learning evaluation.
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.
The paper proposes a method to recommend top performing stocks.
problem Forecasting exact stock returns is difficult due to unpredictable external factors.
method Modified regression model to output rankings of stocks by expected returns.
result The proposed method outperformed other models in recommending top stocks.
CnGAN generates synthetic user preferences for non-overlapped users in cross-network recommender systems.
problem Cross-network recommender solutions ignore non-overlapped users, limiting their applicability.
method Multi-task learning, encoder-GAN architecture, user-based pairwise loss function.
result Generated user preferences improve recommendations for non-overlapped users, achieving superior performance.
Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model w…
Meta decision trees explain user ratings in recommendation systems.
problem Building explainable recommendation systems with clear user explanations.
method Learned regression functions and sparse decision rules based on user embeddings.
result The method provides accurate and interpretable ratings.
The paper briefly introduces multiple classifier systems and describes a new algorithm, which improves classification accuracy by means of recommendation of a proper algorithm to an object classification. This recommendation is done assuming that a classifier is likely to predict the label of the object correctly if it…
We quantify content availability and user discovery opportunities in recommender systems.
problem Determining the maximum probability of recommending content to users.
method Stochastic reachability to compute upper bounds on recommendation likelihood.
result Reachability metrics can detect biases and diagnose user discovery limitations.
System recommends workouts and predicts success rates using RNNs.
problem Promoting healthy lifestyles through personalized exercise recommendations.
method Two interconnected recurrent neural networks (RNNs) using historical workout data.
result Interconnected-RNN model predicts exercise success rates with improved accuracy.
Friend recommendation system using heterogeneous edge embeddings.
problem Inadequate performance of existing network embedding techniques on multi-graph social networks.
method Proposes a method to mine network representation exploiting heterogeneity in multi-graphs.
result Outperforms state-of-the-art baselines on Hike's social network in terms of accuracy and user satisfaction.
NIS learns optimal embedding sizes for recommendation models.
problem Finding optimal embedding sizes for large-scale recommendation models.
method Neural Input Search (NIS) uses reinforcement learning to automatically find optimal vocabulary sizes and embedding dimensions.
result NIS improves prediction accuracy by 6.8% on Recall@1 and 1.8% on ROC-AUC.