In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
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48 results for “Collaborative filtering”
New fairness metrics improve collaborative filtering fairness.
problem Collaborative filtering's bias in historical data leads to unfair predictions for minority groups.
method Identified and proposed four new fairness metrics to address different forms of unfairness.
result Our new metrics better measure fairness than baseline metrics and effectively reduce unfairness.
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filtering suffers from the cold-start problem, which occurs when an item has not yet been rated or a user has not rated any items. Incorporating …
Proposes a deep latent factor model for better recommendation systems.
problem Improving collaborative filtering in recommendation systems.
method Introduces a deeper latent factor model using deep learning.
result Significantly outperforms state-of-the-art techniques in experiments.
Advances in collaborative filtering and ranking methods.
problem Improving recommendation systems efficiency and accuracy.
method Graph information encoding, pairwise and listwise approaches, regularization techniques, personalization.
result New methods significantly improve recommendation system performance.
Efficiently trains autoencoders for collaborative filtering on large datasets.
problem Training autoencoders on large, sparse implicit feedback datasets.
method Mini-batch based negative sampling for large-scale training.
result Shows improved performance compared to state-of-the-art baseline models.
Graph DNA uses Bloom filters to efficiently encode deep graph neighborhoods for better collaborative filtering.
problem Collaborative filtering struggles with exploiting deeper graph neighborhoods due to high time and space complexity.
method Graph DNA employs Bloom filters to compute approximate deep neighborhood information in linear time, enabling efficient encoding and utilization in collaborative filtering.
result Graph DNA significantly improves collaborative filtering performance with minimal computational and memory overhead.
Collaborative filtering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing several collaborative filtering techniques -- both classic and recent state-of-the-…
A recipe recommendation system suggests missing ingredients using collaborative filtering.
problem Encouraging healthy diets through personalized ingredient suggestions.
method Item-based collaborative filtering applied to a sparse dataset of recipes.
result Best method achieves a recall@10 of circa 40%.
Develops NFCF to reduce gender bias in social media recommendation systems.
problem Reduces gender bias in collaborative filtering systems on social media data.
method Pre-training and fine-tuning neural collaborative filtering with bias correction techniques.
result Achieves better performance and fairness in gender de-biased recommendations.
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.
There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation a…
VAEs improve collaborative filtering for implicit feedback.
problem Limited modeling capacity of linear factor models in collaborative filtering.
method Introduced a generative model with multinomial likelihood and used Bayesian inference for parameter estimation.
result Significantly outperforms state-of-the-art baselines on real-world datasets.
Defending Collaborative Filtering Recommenders via Adversarial Robustness Based Edge Reweightingcs.LG
Defense against user shilling attacks in collaborative filtering using edge reweighting.
problem Vulnerability of collaborative filtering to profile injection attacks.
method Adversarial robustness based edge reweighting to attenuate non-robust edges.
result Effective defense against various types of attacks demonstrated through experiments.
New method combines simulated annealing and Levy distribution for fast matrix factorization.
problem High complexity and difficulty in parallelizing matrix factorization for large matrices.
method Combining simulated annealing with Levy distribution for matrix factorization.
result Achieves good solutions in acceptable time with low computations.
Enhances recommendation performance with an ensemble of collaborative filters.
problem Collaborative filtering's performance is unsatisfactory in diverse real-world applications.
method Formulated a probabilistic model integrating items, users, and associations. Derived a progressive algorithm to construct an ensemble of collaborative filters.
result Substantial improvement over state-of-the-art methods, including L2Boost.
Improved collaborative filtering with neural network models of reviews.
problem Improve collaborative filtering performance using side information from reviews.
method Introduced two neural network models (product-of-experts and recurrent neural network) to incorporate reviews into collaborative filtering.
result The product-of-experts model achieved state-of-the-art performance, outperforming LDA-based approach.
We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models. Using the matrix factorization approach to collaborative filtering, the CKF accounts for time evolution by modeling each low-dimensional latent embedding as a multidimensional Brownian motion.…
HCFContext predicts mobile context using collaborative filtering and homomorphic encryption.
problem Accurate mobile context determination for enterprise policies.
method Proposes HPContext and HCFContext models using sequential history and collaborative filtering, with privacy-preserving homomorphic encryption.
result HCFContext enhances context prediction by leveraging related users' observations.
Hybrid VAE improves movie recommendation accuracy.
problem Improving personalized recommendations in online marketplaces.
method Combining movie embeddings from a sibling VAE network with user ratings for movie recommendation.
result Empirical evidence shows VAE network benefits from incorporating movie embeddings.
Collaborative filtering (CF) and content-based filtering (CBF) have widely been used in information filtering applications. Both approaches have their strengths and weaknesses which is why researchers have developed hybrid systems. This paper proposes a novel approach to unify CF and CBF in a probabilistic framework, n…
New model improves recommendation systems by analyzing user-item interactions.
problem Improving recommendation systems for better user-item interactions.
method Sliced Anti-symmetric Decomposition (SAD) model using tensor decomposition.
result SAD produces the most consistent personalized preferences compared to SOTA models.
Collaborative recommendation is an information-filtering technique that attempts to present information items (movies, music, books, news, images, Web pages, etc.) that are likely of interest to the Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and items. In…
New method improves stability of collaborative filtering.
problem Stability issues in matrix approximation for recommender systems.
method Introduces new optimization objectives and solves the optimization problem for stable matrix approximation.
result Achieves better accuracy in rating prediction and top-N recommendation tasks.
Deep autoencoders improve collaborative filtering performance.
problem Rating prediction in recommender systems.
method Deep autoencoder model with 6 layers, end-to-end training, iterative output re-feeding.
result Deep autoencoders outperform shallow models and require regularization.
A new model VCM improves collaborative filtering by synchronously linking two VAEs.
problem Cold start and data sparsity issues in CF-based recommender systems.
method Proposes a variational collaborative model (VCM) that synchronously links two VAEs.
result VCM outperforms state-of-the-art methods on real-life datasets.
Improved product recommendations using deep learning.
problem Sparse customer purchasing data for personalized recommendations.
method Deep Collaborative Filtering (NCF) with latent variables and Bayesian Optimization.
result NCF achieved highest NDCG performance on proprietary dataset.
RGCF improves collaborative filtering by refining graph convolution embeddings.
problem GCN-based recommendation models introduce noise and redundancy, limiting high-order connectivity capture.
method Developed RGCF, a new GCN-based Collaborative Filtering model with redesigned embeddings.
result RGCF significantly outperforms state-of-the-art models on public datasets.
Revisits neural collaborative filtering vs. matrix factorization, showing dot product superiority.
problem Comparing neural collaborative filtering to matrix factorization in recommendation systems.
method Revisited experiments using MLPs as similarity functions, comparing dot product to MLP outputs.
result Simple dot product outperforms MLP-based learned similarities in practical settings.
A new model combines Gaussian processes with collaborative filtering for uncertainty-aware recommendations.
problem Uncertainty in recommendation systems.
method Combining Gaussian process multi-output models with collaborative filtering.
result Generates uncertainty estimates for predictions.
New graph metafeatures improve algorithm selection in collaborative filtering.
problem Selecting the best algorithm for collaborative filtering problems.
method Proposes new graph metafeatures and multicriteria metatargets to evaluate algorithms.
result Graph metafeatures are a good alternative to existing metafeatures.
Improved item recommendation using VAEs with user-dependent priors and text feedback.
problem Improving recommendation quality by integrating user ratings and text feedback.
method Extended VAEs to incorporate user-dependent priors in a multimodal latent space.
result Model outperforms existing VAE models for collaborative filtering (up to 29.41% relative improvement).
Data poisoning attacks can fool neighborhood-based recommender systems.
problem Robustness of neighborhood-based recommender systems to data poisoning attacks.
method Proposed a novel data poisoning attack framework (UNAttack) to fool the systems.
result Neighborhood-based recommender systems can be easily fooled by well-designed fake users.
Collaborative filtering system restores images with impulse noise.
problem Restoring images with identified noisy pixels.
method Defines a new color image representation using matrices and parameters, performs experiments on a database.
result Algorithm performance depends on and parameters, providing guidance on their selection.
We study the stability vis a vis adversarial noise of matrix factorization algorithm for matrix completion. In particular, our results include: (I) we bound the gap between the solution matrix of the factorization method and the ground truth in terms of root mean square error; (II) we treat the matrix factorization as …
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical…
New algorithm for online collaborative filtering using linear bandits and alternating least squares.
problem Online collaborative filtering with item recommendations over time.
method Combines linear bandits and alternating least squares for matrix factorization.
result Superior performance in cumulative regret and average cumulative NDCG over state-of-the-art algorithms.
Flexible priors improve VAE-based CF models for better user preference modeling.
problem Simplistic priors in VAEs limit user preference modeling and deeper representation learning.
method Incorporated flexible priors and gating mechanisms into VAEs for collaborative filtering.
result Flexible priors and gating mechanisms significantly improve recommendation performance.
We learn hierarchical slate representations for collaborative filtering.
problem Building models for recommendation systems with hierarchical slates.
method Learning low-dimensional embeddings of hierarchical slates using recursive composition rules.
result Improved recommendation system performance on a real-world dataset.
Federated Collaborative Filtering preserves user privacy in recommendation systems.
problem Preserving user privacy in machine learning models.
method Federated Learning approach with stochastic gradient updates.
result Collaborative filtering can be successfully federated without accuracy loss.
The article applies empirical Bayes to improve initial parameter choices in collaborative filtering models.
problem Improving initial parameter choices in collaborative filtering models.
method Formulated and implemented empirical Bayes to tune hyperparameters in a Bayesian collaborative filtering setup.
result Empirical Bayes can provide good initial parameter choices, especially for datasets where MCMC struggles.
Integrates CF and RL for collaborative recommendation.
problem Interactive recommendation problem where current recommendations affect future quality.
method Develops CFRL, a novel approach that models as an RL task and learns optimal policy.
result Demonstrates superior performance compared to existing methods on real-world datasets.
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.…
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systemscs.SI
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.
The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class. In this paper we present a novel Bayesian generative model for implicit collaborative filtering. It forms a core component of the Xbox Live architecture, and unlike previous approaches, delineates the o…
New method optimizes collaborative filtering for better ranking metrics.
problem Improving recommendation quality metrics like top-N ranking.
method Actor-critic reinforcement learning to directly optimize ranking metrics.
result The method outperforms state-of-the-art baselines on real-world datasets.
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…
A new neural network model ONCF improves collaborative filtering by explicitly modeling embedding correlations.
problem Improving collaborative filtering for better recommendation quality.
method ONCF uses an outer product to model embedding correlations and a convolutional layer to learn high-order correlations.
result ONCF outperforms existing models in implicit feedback data experiments.