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…
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48 results for “clustered users”
In this paper, we consider a popular model for collaborative filtering in recommender systems where some users of a website rate some items, such as movies, and the goal is to recover the ratings of some or all of the unrated items of each user. In particular, we consider both the clustering model, where only users (or…
Optimal algorithm for latent bandits with cluster structure reduces regret to nearly optimal.
problem Maximizing cumulative rewards in a multi-armed bandit problem with latent clusters.
method LATTICE algorithm exploiting cluster structure and arm information.
result Minimax optimal regret of with calls to matrix completion oracle.
PinnerSage creates multi-modal user embeddings for better Pinterest recommendations.
problem Single high-dimensional embeddings fail to fully understand user interests.
method Hierarchical clustering and Medoids to summarize user actions into coherent clusters.
result PinnerSage outperforms single embedding methods in personalized recommendations.
Interactive steering improves hierarchical clustering for diverse user needs.
problem Existing hierarchical clustering methods fail to meet diverse user needs.
method Knowledge-driven and data-driven constraints, interactive steering through a visual interface.
result Facilitates the building of customized clustering trees efficiently and effectively.
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 for well-separated clusters and for poorly separated clusters.
Much of the data being created on the web contains interactions between users and items. Stochastic blockmodels, and other methods for community detection and clustering of bipartite graphs, can infer latent user communities and latent item clusters from this interaction data. These methods, however, typically ignore t…
Optimal recommendation system using user and item clustering.
problem Maximizing recommendation accuracy with limited feedback.
method Latent variable model with user and item clustering, exploiting i.i.d. structure.
result Near-optimal algorithm that combines item and user structures.
New algorithms improve decision-making with limited offline data.
problem Using limited offline data to cluster users for better decision-making.
method Proposed two algorithms: Off-C2LUB and Off-CLUB to address data insufficiency.
result Both algorithms outperform existing methods under limited offline user data.
A new algorithm for efficient clustered federated learning.
problem Federated learning with clustered users having different learning tasks.
method Iterative Federated Clustering Algorithm (IFCA) that alternates cluster estimation and model parameter optimization.
result IFCA converges with good initialization and guarantees optimal statistical error rate.
Introduces BCVI, a Bayesian cluster validity index for better cluster selection.
problem Selecting the optimal number of clusters in clustering algorithms.
method Develops BCVI using Dirichlet or generalized Dirichlet priors, evaluated against existing indices.
result BCVI offers clear advantages in scenarios where user expertise is valuable.
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.
Improved algorithm for clustered Federated Learning reduces initialization and hyperparameter requirements.
problem Dichotomy between heterogeneous models and simultaneous training in Federated Learning.
method Proposes a new clustering framework and an improved algorithm ( exttt{SR-FCA}) that removes restrictive assumptions.
result Improves clustering accuracy and removes the need for good initialization and hyperparameters.
FISHDBC is a flexible, incremental, scalable, and hierarchical density-based clustering algorithm. It is flexible because it empowers users to work on arbitrary data, skipping the feature extraction step that usually transforms raw data in numeric arrays letting users define an arbitrary distance function instead. It i…
We present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through u…
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.
We address the problem of communicating domain knowledge from a user to the designer of a clustering algorithm. We propose a protocol in which the user provides a clustering of a relatively small random sample of a data set. The algorithm designer then uses that sample to come up with a data representation under which …
Constraint-based clustering algorithms exploit background knowledge to construct clusterings that are aligned with the interests of a particular user. This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or n…
Given a set of pairwise comparisons, the classical ranking problem computes a single ranking that best represents the preferences of all users. In this paper, we study the problem of inferring individual preferences, arising in the context of making personalized recommendations. In particular, we assume that there are …
Efficiently models agent dependencies in large social networks.
problem Challenges in incorporating inter-agent dependencies in social reinforcement learning models.
method Clusters users based on payoff and contribution, combines cluster-level policies with personalized agent-level policies, and uses dynamic clustering.
result Our approach learns more accurate policy estimates and converges more quickly than existing methods.
We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies. A more efficient algorithm is proposed with simple set structures to represent clusters. We prove a regret bound for the new algorithm which is free of the minimal frequency over users. The experiments …
We propose some axioms for hierarchical clustering of probability measures and investigate their ramifications. The basic idea is to let the user stipulate the clusters for some elementary measures. This is done without the need of any notion of metric, similarity or dissimilarity. Our main results then show that for e…
A new federated learning method clusters users into multiple models for better data distribution handling.
problem Non-IID data from heterogeneous sources in federated learning.
method Proposes a multi-center aggregation mechanism to learn multiple global models and optimally match users to centers.
result Our method outperforms existing federated learning methods on benchmark datasets.
We study the problem of clustering a set of items from binary user feedback. Such a problem arises in crowdsourcing platforms solving large-scale labeling tasks with minimal effort put on the users. For example, in some of the recent reCAPTCHA systems, users clicks (binary answers) can be used to efficiently label imag…
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limited by logistics and expense. Constrained clustering, as a subclass of SSL, makes use of user input in the form of relationships between data …
Community Detection in Political Twitter Networks using Nonnegative Matrix Factorization Methodscs.SI
Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate th…
System uses TDA for user segmentation and demand forecasting.
problem User loyalty and demand forecasting challenges.
method TDA-based clustering of time series data with matrix factorization.
result Significantly higher accuracy in clustering and demand forecasting.
Model clusters authors and topics in short texts like social media posts.
problem Analysis of short texts is difficult due to brevity and lack of context.
method Expands Latent Dirichlet Allocation to model word dependencies and cluster users.
result Improves topic and user clustering, outperforming traditional methods.
Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful integration of clustering approaches in automated decision-support systems hinges on the interpretability of the resulting clusters. This pape…
Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset. Semi-supervised clustering addresses this by allowing the user to provide examples of instances that should (not) be in the same cluster. …
In this paper, we introduce a methodology that allows to model behavioral trajectories of users in online social media. First, we illustrate how to leverage the probabilistic framework provided by Hidden Markov Models (HMMs) to represent users by embedding the temporal sequences of actions they performed online. We the…
Three approaches for personalized models in machine learning.
problem Training a single model for all users is not optimal in many scenarios.
method User clustering, data interpolation, and model interpolation.
result Learning-theoretic guarantees and efficient algorithms for personalized models.
It is often the case that, within an online recommender system, multiple users share a common account. Can such shared accounts be identified solely on the basis of the userprovided ratings? Once a shared account is identified, can the different users sharing it be identified as well? Whenever such user identification …
Mining Permission Request Patterns from Android and Facebook Applications (extended author version)cs.CR
Android and Facebook provide third-party applications with access to users' private data and the ability to perform potentially sensitive operations (e.g., post to a user's wall or place phone calls). As a security measure, these platforms restrict applications' privileges with permission systems: users must approve th…
New distributed clustering algorithms show resilience to initialization issues.
problem Resilience of distributed gradient-based clustering algorithms to center initialization.
method Distributed gradient-based clustering algorithms with novel center initialization.
result The algorithms are more resilient to initialization compared to baseline methods.
Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. For most applications, applying clustering is only appropriate when cluster structure is present. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of clus…
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.…
A new method selects important variables for clustering from dependency networks.
problem Variable selection for clustering in high-cost data scenarios.
method Create dependency networks, rank variables by centrality, select top-n variables.
result Top-n variables improve clustering performance compared to existing 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…
We present a method for a wine recommendation system that employs multidimensional clustering and unsupervised learning methods. Our algorithm first performs clustering on a large corpus of wine reviews. It then uses the resulting wine clusters as an approximation of the most common flavor palates, recommending a user …
One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these situations, we decided to work on the task of recommending hotels to users. We used…
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experimentstat.ML
In order to be useful, visualizations need to be interpretable. This paper uses a user-based approach to combine and assess quality measures in order to better model user preferences. Results show that cluster separability measures are outperformed by a neighborhood conservation measure, even though the former are usua…
The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection. However, in real-life applications the number of clusters or communities (say, ) is generally unknown a-priori. Consequently, the majority of …
Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering process. Typically, this supervision is provided by the user in the form of pairwise constraints. Existing methods use such constraints in one of the following ways: they adapt their clustering procedure, their similarity…
Exploiting low-rank structure of the user-item rating matrix has been the crux of many recommendation engines. However, existing recommendation engines force raters with heterogeneous behavior profiles to map their intrinsic rating scales to a common rating scale (e.g. 1-5). This non-linear transformation of the rating…
New method for online low-rank matrix completion with improved regret.
problem Designing an efficient algorithm for online recommendation systems with low regret.
method Explore-then-commit (ETC) approach and iterative user clustering (OCTAL) for rank-1 setting.
result Nearly optimal regret bounds for online low-rank matrix completion.
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…
We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner. CAB makes sharp departures from the state of the art by incorporating collaborative effects into …