DBRec discovers latent groups to improve recommendation.
problem Sparse user-item interaction data in recommender systems.
method Simultaneously discovers latent user/item groups and interacts them with users/items for bridging preferences.
result DBRec outperforms state-of-the-art models on real datasets.
We introduce a novel latent grouping model for predicting the relevance of a new document to a user. The model assumes a latent group structure for both users and documents. We compared the model against a state-of-the-art method, the User Rating Profile model, where only users have a latent group structure. We estimat…
Owners of a web-site are often interested in analysis of groups of users of their site. Information on these groups can help optimizing the structure and contents of the site. In this paper we use an approach based on formal concepts for constructing taxonomies of user groups. For decreasing the huge amount of concepts…
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.
COHORTNEY groups web users based on activity patterns.
problem Lack of academic discussion on cohort analysis for user behavior.
method Unsupervised non-parametric machine learning approach.
result COHORTNEY outperforms traditional methods in cohort analysis.
Paper proposes using taste groups for better item recommendations.
problem Lack of negative examples in implicit feedback.
method Hierarchical Latent Tree Analysis (HLTA) to identify taste-based user groups.
result Recommendations for a user based on her memberships in identified taste groups.
Machine learning models can lead to group representation disparity, affecting long-term retention.
problem Representation disparity in ML models can lead to group retention issues over time.
method Analyzed user dynamics and fairness criteria in a sequential decision-making framework.
result Representation disparity can worsen over time without proper fairness criteria.
Algorithm maximizes user rewards under per-item budget constraints.
problem Maximizing cumulative rewards in collaborative bandits with budget constraints.
method Collaborative algorithm B-LATTICE that clusters users and collaborates across groups.
result Achieves sub-linear regret bounds matching minimax bounds.
Paper proposes TPathMine model for more accurate user attribute prediction.
problem Predicting user attributes from click data in heterogeneous networks.
method HetPathMine model with meta-path weights optimized for user emotional preferences.
result TPathMine model achieves higher accuracy in user attribute prediction.
FedCBO solves clustered federated learning by optimizing groups of users without knowing their structure.
problem Training models for multiple users with privacy and communication constraints, especially in clustered settings.
method FedCBO uses a particle system approach inspired by consensus-based optimization to train models for each user group.
result FedCBO outperforms other methods in training models for clustered federated learning.
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.
Generative methods for creating new items for user groups.
problem Creating new items for groups of users with varying preferences.
method Formalized joint problem, used VAE latent space for item generation and user group prediction.
result Generated items similar to highly desirable unobserved items.
This work optimizes mean estimation under varying privacy constraints.
problem Mean estimation with heterogeneous privacy constraints.
method Proposes an algorithm for mean estimation under different privacy levels for users.
result Shows a saturation phenomenon in performance as privacy levels are relaxed.
Paper analyzes privacy-aware mobility behavior using entropy metrics.
problem Intrusive user location tracking makes it easy to identify users.
method Proposes spatio-temporal entropy to quantify mobility, uses GAMs to study effects of variables.
result Global GAM provides more accurate predictions of spatio-temporal entropy.
Study shows significant differences in recommendation bias between model-based and memory-based algorithms.
problem Recommendation bias disparity across different algorithms and item categories.
method Examined bias disparity in a range of collaborative recommendation algorithms and item categories.
result Significant differences found between model-based and memory-based algorithms.
In this paper we present a review of the existing typologies of Internet service users. We zoom in on social networking services including blogs and crowdsourcing websites. Based on the results of the analysis of the considered typologies obtained by means of FCA we developed a new user typology of a certain class of I…
Adaptive clustering and personalization algorithms minimize regret in multi-agent stochastic linear bandits.
problem Minimizing regret in a multi-agent stochastic linear bandits framework with user heterogeneity.
method Proposes a novel algorithm that refines cluster identities and minimizes regret, adapting to cluster separation and user parameter deviations.
result Regret scales as O ( T / N ) \mathcal{O}(\sqrt{T/N}) O ( T / N ) for well-separated clusters and O ( T 1 2 + ε / ( N ) 1 2 − ε ) \mathcal{O}(T^{\frac{1}{2} + \varepsilon}/(N)^{\frac{1}{2} -\varepsilon}) O ( T 2 1 + ε / ( N ) 2 1 − ε ) for poorly separated clusters. This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track 2) for context-aware movie recommendation systems. The train dataset comprises 4,536,891 ratings provided by 171,670 users on 23,974$ movies, as well as the household groupings of a subset of the users. The test dataset comprises 5,450 rating…
We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users. Our algorithm dynamically groups users based on their observed behavioral similarity during a sequence of logged activities. In doing s…
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.
User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who…
Dual-view mixture models cluster users with features and latent behaviors inferred from actions.
problem Clustering users based on features and latent behavioral functions inferred from indirect observations.
method Dual-view mixture models with non-parametric Dirichlet Process for automatic cluster number inference.
result Dual-view models outperform single-view models when one view lacks information.
FedUA trains UA models privately without raw data.
problem Training UA models requires raw user data, compromising privacy.
method Federated learning framework for privacy-preserving UA model training.
result FedUA reliably rejects unseen user data at high true positive rates.
Fairness in AI decisions for users with varying performance.
problem Ensuring fairness in AI decisions for users with different performance levels.
method Contextual Multi-Armed Bandit algorithm with fairness constraints.
result Accounting for user contexts improves fairness in AI decisions.
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.
New models automate support group formation in online health communities.
problem Challenges in traditional support group formation methods for scalability, static categorization, and insufficient personalization.
method Two novel machine learning models: gDMR and gSTM, integrating user content, demographics, and network data.
result Models outperform baselines in predictive accuracy, semantic coherence, and internal group consistency.
A new reinforcement learning framework separates users into risk-tolerant and risk-averse groups for better performance.
problem Improving performance for risk-averse users in reinforcement learning.
method Introducing a tiered reinforcement learning approach with two policies: π e x t O π^{ ext{O}} π e x t O and π e x t E π^{ ext{E}} π e x t E . result Achieving constant regret for risk-averse users, independent of the number of episodes.
Detects anomalous behavior in social media users by analyzing content and connections.
problem Identifying disruptive patterns in user behavior on social media platforms.
method Joint representation learning of content and connection to detect anomalous behavior.
result Observed densely connected users engaging in local politics and exhibiting troll-like behavior.
This work tackles semi-supervised federated learning by reducing model gradient diversity.
problem Improving test accuracy in semi-supervised federated learning with limited labeled data.
method Investigates and compares various design choices including consistency regularization loss, Batch Normalization, and Group Normalization.
result Grouping-based model averaging combined with Group Normalization and consistency regularization loss improves test accuracy.
The study explains YouTube commenters' behavior using rational inattention models.
problem Understanding and predicting YouTube commenters' behavior.
method Deep embedded clustering for user grouping, Bayesian revealed preferences for rationality testing, and behavioral economics constraints for attention span modeling.
result Most YouTube user groups optimize a Bayesian utility with rationally inattentive constraints.
Turbo-Aggregate reduces secure aggregation time from quadratic to nearly linear.
problem Quadratic overhead in secure model aggregation for federated learning.
method Multi-group circular strategy, additive secret sharing, and coding techniques.
result Achieves O ( N log N ) O(N\log{N}) O ( N log N ) overhead, compared to O ( N 2 ) O(N^2) O ( N 2 ) , for up to 50% user dropout. Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid.…
Identifies influential users in collaborative filtering systems.
problem Impact of influential users on collaborative filtering systems.
method Formalized influence, identified influential users, analyzed impact across domains.
result Identified and characterized influential users and analyzed their impact.
Weibo experts predict stock market better than non-experts.
problem Improving stock market prediction accuracy using sentiment analysis.
method Combining BERT for sentiment classification and LSTM for time-series prediction on Weibo data.
result AFA group users' predictions are 39.67% more accurate than UFA group users.
Study uses time series analysis to predict player churn and conversion in games.
problem Predicting player churn and conversion in free-to-play games.
method State Space time series approach with Autoregressive Integrated Moving Average and Unobserved Components models.
result Unobserved Components approach fails to detect marketing campaigns and predicts abandonment poorly.
New algorithms for batch decision-making with high-dimensional user data.
problem Maximizing treatment efficacy in groups of users.
method Teamwork LASSO Bandit algorithm, switching between teamwork and selfish stages.
result Upper bound on expected cumulative regret for the proposed algorithm.
The paper clusters hypergraphs to find diverse and experienced groups based on past experiences.
problem Finding diverse and experienced groups with respect to past experiences.
method Regularized edge-based hypergraph clustering objective with a 2-approximation algorithm.
result Demonstrates an efficient 2-approximation algorithm for clustering hypergraphs.
This study measures liquidity risks in Aave, a blockchain lending protocol.
problem Liquidity risks in lending protocols, especially in Aave.
method Measurements of liquidity risks using Aave as a case study, focusing on available liquidity and market concentration.
result Liquidity risks in Aave are volatile and affect the protocol negatively, especially for repeat borrowers.
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.
Predict player lifetime value based on engagement metrics.
problem User profiling in video games.
method Survival curves, deep learning, long short-term memory.
result Deep learning predicts player lifetime value.
WassFFed addresses fairness in Federated Learning by ensuring consistency between local and global models.
problem Achieving fairness in Federated Learning where data is distributed among diverse user groups.
method WassFFed employs a Wasserstein barycenter calculation to aggregate local models' outputs, ensuring consistency and fairness.
result WassFFed outperforms existing approaches in balancing accuracy and fairness.
Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user. The more the number of rated examples giv…
Predicts user age and gender on Tumblr using rich content.
problem Challenges in targeting specific demographic groups on Tumblr.
method Graph based and deep learning models, including network embedding, label propagation, CNN, and MLP.
result Significantly improved accuracy for age and gender predictions.
Most industrial recommender systems rely on the popular collaborative filtering (CF) technique for providing personalized recommendations to its users. However, the very nature of CF is adversarial to the idea of user privacy, because users need to share their preferences with others in order to be grouped with like-mi…
Machine learning models (e.g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e.g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss. Worse, as model accuracy affects user retention, a minor…
Proposes a model to recommend products at the right time to meet user demands.
problem Maximizing product sales by recommending products at the right time to meet user demands.
method Integrates user interests and time-based demands into a Long-Short Demands-Aware Model (LSDM) using recurrent neural networks.
result Demonstrates the effectiveness of the LSDM in next-item recommendation on real-world commerce datasets.
The paper analyzes user activities in OSNs using a vector space model.
problem Understanding user interactions and activity patterns in OSNs.
method TF-IDF scheme of Vector Space Model to analyze object-viewer relationships.
result Identified activity relationships among users and objects in OSNs.
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.