Algorithm learns diverse recommendations from partial clicks.
problem Learning diverse recommendations from partial user feedback.
method Online learning algorithm for cascade model.
result Gap-free upper bound on n-step regret proved.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
Trend · papers per month
48 results for “diverse recommendations”
The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the recommendations, as relevant items in the list may be replaced by more diverse ones. In this work we propose a novel method for maximizing the u…
RNE tackles scalable recommendation for billion-scale scenarios.
problem Designing a scalable recommendation system for diverse and dynamic user interests.
method RNE uses a diversity- and dynamics-aware neighbor sampling method for scalable network embedding.
result RNE achieves high-quality and diverse results on a billion-scale user-item graph.
Proposes a new model for diversified interactive recommendation.
problem Previous methods focus on optimizing recommendation accuracy, ignoring diversity.
method DCB model using determinantal point process and Thompson sampling-based variational Bayesian inference.
result Demonstrates effectiveness of the proposed method through extensive experiments.
ManifoldMind uses adaptive-curvature probabilistic spheres for trustworthy recommendations in semantic hierarchies.
problem Sparse and abstract recommendation domains where users explore diverse conceptual paths.
method Adaptive-curvature probabilistic spheres, soft multi-hop inference, and curvature-aware semantic kernel.
result Superior NDCG, calibration, and diversity compared to baselines on public benchmarks.
The paper improves recommendation systems by ensuring their outputs are reliable.
problem Recommendation systems often lack reliability guarantees for their outputs.
method The method uses a pre-trained ranking model to create a set of items with rigorous FDR control.
result The approach provides a way to guarantee the reliability of recommendation outputs.
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.
A recommendation framework helps users choose healthcare interventions.
problem Choice overload in online healthcare communities.
method Multi-Armed Bandit (MAB) approach with innovative model components.
result Our recommendation design outperforms state-of-the-art systems.
Paper tackles online ranking and diversification in recommender systems.
problem Maximizing relevance and diversity in ranked lists for online recommendation.
method CascadeHybrid approach that combines contextual bandits for relevance and topical diversity.
result CascadeHybrid outperforms baselines in real-world datasets.
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent k…
Advances citation and subject label recommendation using multi-modal adversarial autoencoders.
problem Improving recommendation systems for citations and subject labels.
method Multi-modal adversarial autoencoders with adversarial regularization, sparsity, and input modality analysis.
result Adversarial regularization consistently improves recommendation performance.
Neural M3 model adapts to diverse user behaviors over short and long timeframes.
problem Adapting to diverse user behaviors over short and long timeframes.
method Neural Multi-temporal-range Mixture Model (M3) combining short-term and long-term models with a learned gating mechanism.
result M3 consistently outperforms state-of-the-art sequential recommendation methods.
MEANTIME improves sequential recommendation by using multi-temporal embeddings and attention mechanisms.
problem Limited use of timestamp information and information bottleneck in sequential recommendation models.
method MEANTIME employs multiple types of temporal embeddings and attention mechanisms to capture diverse patterns from user behavior sequences.
result MEANTIME outperforms state-of-the-art sequential recommendation methods.
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the use…
A new algorithm uses bandits to diversify database activity monitoring.
problem Limitation of current DAM systems in collecting diverse data.
method Redefined DAM sampling as a bandit problem and developed a novel algorithm combining expert knowledge and random exploration.
result Adding diversity to sampling using the bandit-based approach improves coverage without decreasing alert quality.
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.
Proposes a graph-based system for personalized news recommendation considering multiple user behaviors.
problem Lack of considering multiple user behaviors in news recommendation systems.
method Builds an interaction behavior graph, applies DeepWalk and G-CNN for news and behavior sequence representations, introduces core and coritivity features.
result Achieves personalized news recommendation considering user's concentration degree of interests.
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.
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.
Recommenders have become widely popular in recent years because of their broader applicability in many e-commerce applications. These applications rely on recommenders for generating advertisements for various offers or providing content recommendations. However, the quality of the generated recommendations depends on …
MIND models user interests with multiple vectors for better recommendation.
problem Insufficient representation of user interests in deep learning models.
method Multi-Interest Network with Dynamic routing (MIND) using capsule routing and label-aware attention.
result MIND achieves superior performance in recommendation compared to state-of-the-art methods.
Improved UCB algorithm for diversity in bandits with lower bounds.
problem Improving diversity in bandit-based recommendations.
method Designing a UCB algorithm exploiting the specific structure of the problem.
result The UCB algorithm achieves bounded distribution-dependent regret under certain conditions.
Paper presents a deterministic method for diverse subset selection.
problem Diverse subset selection problems in recommendation, summarization, and search.
method Greedy deterministic adaptation of k-DPP for low-rank approximations and image search.
result The method yields low-rank approximations of kernel matrices and demonstrates effectiveness in image search.
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.
LSTM improves cross-network recommendations by capturing user preference changes and irregular time intervals.
problem Offline cross-network recommender solutions fail to capture user preference changes and dynamic environments.
method Proposes a multi-layered LSTM network with attention mechanisms, higher order interactions, and time-aware gates.
result The model consistently outperforms state-of-the-art in accuracy, diversity, and novelty.
MGDRec optimizes multiple objectives in recommender systems.
problem Optimizing for multiple, often conflicting objectives in recommender systems.
method Stochastic multi-gradient descent approach (MGDRec).
result MGDRec outperforms state-of-the-art methods in traditional objective mixtures.
Making recommendations in the presence of sparsity is known to present one of the most challenging problems faced by collaborative filtering methods. In this work we tackle this problem by exploiting the innately hierarchical structure of the item space following an approach inspired by the theory of Decomposability. W…
MARS-Gym framework for marketplaces to train and evaluate recommender systems.
problem Challenges in designing, training, and evaluating recommender systems in marketplaces.
method Open-source framework for Reinforcement Learning agents in marketplaces.
result Empowers researchers and engineers to quickly build and evaluate agents for recommendations.
Many latent (factorized) models have been proposed for recommendation tasks like collaborative filtering and for ranking tasks like document or image retrieval and annotation. Common to all those methods is that during inference the items are scored independently by their similarity to the query in the latent embedding…
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.
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.
JIMA uses multi-level preference data to recommend composite items.
problem Recommending composite items efficiently with multi-level preference information.
method Joint Interaction Modeling (JIMA) approach that integrates multi-level preference data and interactions.
result JIMA outperforms advanced baselines in offline and online settings.
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recomme…
Determinantal point processes (DPPs) have garnered attention as an elegant probabilistic model of set diversity. They are useful for a number of subset selection tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. In this work we present a new method for learning th…
EMDE efficiently estimates manifold densities for diverse recommendation systems.
problem Efficiently estimating manifold densities for multi-modal recommendation systems.
method EMDE (Efficient Manifold Density Estimator) framework for arbitrary vector representations.
result Established new state-of-the-art results in top-k and session-based recommendation settings.
Unified neural framework for multi-relational recommender systems.
problem Accurately capturing users' fine-grained preferences from diverse feedback types.
method Multi-Relational Memory Network (MRMN) framework that models fine-grained user-item relations and discriminates between feedback types.
result The proposed MRMN model outperforms state-of-the-art algorithms in various recommender scenarios.
Optimizes long-term social welfare in recommender systems by matching users to providers.
problem Realistic recommender systems dynamics affect all agents, not just users.
method Formulated as an optimal constrained matching problem, solved using dynamical system equilibrium selection.
result Ensures maximal social welfare with diverse viable providers, improving over myopic matching.
HyperFair integrates fairness in recommender systems using probabilistic soft logic.
problem Ensuring fairness in recommender systems across diverse domains.
method Soft fairness constraints integrated as regularization in a joint inference objective function.
result HyperFair improves fairness of predictions from black-box models and hybrid systems.
Recommender systems are widely used to predict personalized preferences of goods or services using users' past activities, such as item ratings or purchase histories. If collections of such personal activities were made publicly available, they could be used to personalize a diverse range of services, including targete…
On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyondcs.LG
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.
Matrix factorization is a simple and effective solution to the recommendation problem. It has been extensively employed in the industry and has attracted much attention from the academia. However, it is unclear what the low-dimensional matrices represent. We show that matrix factorization can actually be seen as simult…
Collaborative filtering is an important technique for recommendation. Whereas it has been repeatedly shown to be effective in previous work, its performance remains unsatisfactory in many real-world applications, especially those where the items or users are highly diverse. In this paper, we explore an ensemble-based f…
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.
A new clustering framework optimizes customer search data for personalized travel recommendations.
problem Personalized travel recommendations based on customer search data.
method Multi-objective optimization-based clustering ensemble framework.
result Optimizes diversity in clustering ensemble search space and automatically determines the number of clusters.
This paper improves attacks on recommender systems by solving optimization problems more precisely.
problem Adversarial injection attacks on recommender systems.
method Solving optimization problems for generating fake user interactions more accurately.
result The exact solution for generating fake users leads to a much larger impact on recommender systems.
SVAT reduces investment risks by making stock models sensitive to adversarial perturbations.
problem Risk control in stock recommendation models is insufficient, leading to high investment losses.
method SVAT combines adversarial learning and variational perturbation generation to enhance risk awareness.
result SVAT reduces investment risks by more than 30% compared to state-of-the-art baselines.
A new method for multi-criteria recommender systems using graph attention networks.
problem Lack of nuanced relationships between users and items based on specific criteria.
method MDGAT, a multi-edge bipartite graph with dual attention networks and contrastive learning.
result MDGAT achieves higher accuracy in predicting item ratings compared to baseline methods.
The paper presents a method for personalized exercise recommendations that improves learner skill gain.
problem Adapting to individual needs in large, diverse groups of learners in digital environments.
method Contextual Thompson Sampling to select exercises that advance learner skill.
result The method recommends exercises associated with greater skill improvement and adapts to learner differences.