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.
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. …
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.
Graph neural networks improve cold start for new items in recommender systems.
problem Cold start problem for new items in recommender systems.
method Item hierarchy graphs and bespoke graph neural network architecture.
result Our method achieves better forecasting quality than state-of-the-art with comparable computational time.
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.
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.
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.
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.
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 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.
The paper introduces metrics and a regularization method to improve fairness in recommendation rankings.
problem Fairness risks in recommender systems that match users to products or information.
method Pairwise comparisons from randomized experiments to quantify and address fairness concerns in rankings.
result Improvement in pairwise fairness of a production recommender system through regularization.
Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local inf…
This work explores the ability of collective matrix factorization models in recommender systems to make predictions about users and items for which there is side information available but no feedback or interactions data, and proposes a new formulation with a faster cold-start prediction formula that can be used in rea…
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.
A graph traversal algorithm for cold-start news recommendation using named entities.
problem Cold-start news recommendation for articles without user-specific information.
method Graph traversal algorithm and novel weighting scheme for named entities over a knowledge graph.
result Our method produces stronger Pearson correlation to human similarity scores than other cold-start methods.
New approach to disentangle utility from impulse in recommendation systems.
problem Difficulty in inferring user utility from engagement signals.
method Generative model based on self-exciting Hawkes process to infer utility from return probability.
result It is possible to disentangle System-1 and System-2 decision processes to optimize content based on user utility.
This study improves user segmentation for online news recommendation systems.
problem Challenges in building modern recommender systems due to dynamic environments and data sparsity.
method Trend-responsive unsupervised user segmentation using multi-armed bandits.
result Significant improvements in online A/B tests compared to global-optimization algorithms.
This paper builds a recommendation system for borrowers on P2PL platforms to lower interest rates.
problem Help borrowers make wise decisions on P2PL platforms to achieve lower interest rates.
method Developed a recommendation system to suggest the type of loan borrowers should apply for.
result Borrowers can achieve lowered interest rates with a higher likelihood of getting funded using the recommendation system.
News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and…
Paper presents content-based models for game recommendation in cold start scenarios.
problem Cold start problem in game recommendation where new games and players have no historical data.
method Uses survey data to develop content-based interaction models that generalize to new games, players, and both.
result Content models outperform collaborative filtering in predicting new interactions.
A new personality-based recommender system tackles data sparsity without feedback.
problem Data sparsity without common feedback among users.
method Implicitly identifying users' personality type and incorporating it with personal interests and knowledge level.
result The model's effectiveness, especially in data sparsity situations, demonstrated on a real-world dataset.
Recommender systems have recently attracted many researchers in the deep learning community. The state-of-the-art deep neural network models used in recommender systems are typically multilayer perceptron and deep Autoencoder (DAE), among which DAE usually shows better performance due to its superior capability to reco…
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressi…
New bandit model accounts for user departures in recommender systems.
problem Capturing user departures in recommender systems with multi-armed bandits.
method Proposes a novel multi-armed bandit setup with two types of users and analyzes optimal and efficient algorithms.
result Achieves optimal and efficient learning algorithms for user types and reward probabilities.
The paper introduces recklessness to improve recommendation quality and quantity.
problem The reliability/coverage dilemma in recommender systems limits the number of recommended items.
method Incorporates a new term (recklessness) into matrix factorization-based recommender systems to address the dilemma.
result Recklessness improves the quantity and quality of recommendations by allowing for risk regulation.
A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Tensor-based recommender models push the boundaries of traditional collaborative filtering techniques by taking into account a multifaceted nat…
New method for evaluating sequential recommendations with lower variance.
problem Evaluating good sequences of music, video, news, and e-commerce recommendations.
method Proposes a new counterfactual estimator for sequential reward interactions with lower variance and asymptotic unbiasedness.
result Our method outperforms existing methods in bias and data efficiency for sequential track recommendations.
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…
New method to rank metrics on non-shuffled traffic.
problem Position bias in ranking metrics due to item display order.
method Leverage stochasticity of recommendation policy to mitigate position bias.
result Improved ranking metrics without shuffling recommendations.
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.
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.
A new recommendation system model tackles unreliable user behavior.
problem Creating effective recommendation systems in the presence of unreliable user behavior.
method A novel modification of Multi-Armed Bandits with an unreliable intermediate.
result Proved fundamental theorems and developed an Explore-Commit algorithm close to optimal performance.
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.
This paper uses cPF to build recommender systems from raw count data.
problem Sparse, over-dispersed and bursty count data make direct use in recommender systems challenging.
method Compound Poisson Factorization (cPF) with a unified framework (dcPF) and adaptive algorithm.
result dcPF achieves better recommendation scores than Poisson Factorization on raw or binarized data.
Many modern commercial sites employ recommender systems to propose relevant content to users. While most systems are focused on maximizing the immediate gain (clicks, purchases or ratings), a better notion of success would be the lifetime value (LTV) of the user-system interaction. The LTV approach considers the future…
Machine learning biases in job recommendations can lead to unfair outcomes.
problem Biased recommendations from recommender systems in job matching.
method Addressing biases at various stages of recommender systems training and deployment.
result Techniques can reduce bias in job recommendations, ensuring fair outcomes.
New system assigns vehicles to routes for cost and energy efficiency.
problem Optimizing vehicle route assignment for cost and energy efficiency.
method Machine learning-based neural network algorithm to estimate energy consumption and provide real-time recommendations.
result Demonstrated efficient vehicle assignment for medium and heavy duty trucks.
A new recommendation method combining deep learning and graph analysis improves performance.
problem Challenges in recommender systems like cold-start and sparsity.
method Matrix Factorization, deep Autoencoders, and deep embedding methods.
result The proposed method outperforms existing state-of-the-art methods.
The paper proposes a new recommender system combining ratings and textual reviews.
problem Lack of using textual reviews in recommender systems.
method Combines Latent Factor Model with Latent Dirichlet Allocation for textual reviews.
result Combining textual reviews with ratings improves recommendation quality.
In this paper we propose two new algorithms based on biclustering analysis, which can be used at the basis of a recommender system for educational orientation of Russian School graduates. The first algorithm was designed to help students make a choice between different university faculties when some of their preference…
REN addresses uncertainty in user feedbacks for better recommendation systems.
problem Recurrent neural networks focus solely on item relevance, neglecting diverse item exploration.
method Proposes REN, a new type of recurrent neural network that balances relevance and exploration while accounting for representation uncertainty.
result REN achieves satisfactory long-term rewards on synthetic and real-world recommendation datasets, outperforming state-of-the-art models.
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.
XploVAE improves recommendation by balancing known and novel items.
problem Balancing known and novel items for better recommendations.
method Constructs user-specific subgraphs for exploitation and exploration, learns personalized item embeddings.
result Demonstrates effectiveness on various real-world datasets.
Scalable hyperbolic recommender system outperforms Euclidean models on complex networks.
problem Complex network datasets require more efficient recommendation models.
method Used hyperbolic geometry, novel hyperbolic model, and Einstein midpoint for asymmetric recommendations.
result Hyperbolic recommender systems significantly outperform Euclidean models on complex network datasets.
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…
A new method for decision tree selection in recommendation systems.
problem Feature-based selection of a single tree from an ensemble for dynamic interpretation.
method A multi-armed contextual bandit recommendation framework that trains a system on top of Random Forests to identify the most relevant tree.
result The dynamic method outperforms an independent CART tree and is comparable to Random Forest in predictive performance.
Proposes a deep learning model for timely and accurate recommendations.
problem Inability to provide timely recommendations and ranking issues with implicit feedback.
method Unified cross-network solution using listwise ranking for implicit data.
result Superior performance in accuracy, novelty, and diversity compared to baselines.