Paper tackles item cold-start problem using active learning and attributes.
problem Cold-start issue in recommender systems for new items.
method Proposes a novel recommendation scheme combining active learning and item attributes.
result Experimental results show superior performance compared to traditional methods.
New cold-start recommendation method for recommender systems.
problem Making accurate recommendations for users and items with limited data.
method Proposes a faster cold-start prediction formula in collective matrix factorization models.
result Improved cold-start recommendations with reliable predictions for new users, better than non-personalized recommendations.
A method for cold-start playlist recommendation using multitask learning.
problem Playlist recommendation for new users and songs.
method Flexible multitask learning method that minimizes a bipartite ranking loss and a classification loss.
result The method performs well in cold-start scenarios on real datasets.
Memory-Augmented Meta-Optimization improves cold-start recommendation.
problem Cold-start problem in recommender systems for new users or items.
method Memory-Augmented Meta-Optimization approach with personalized and task-specific memories.
result Significant improvement in cold-start recommendation performance on multiple datasets.
Proposes efficient user cold start recommendation via meta parameter partition.
problem User cold start in recommendation systems.
method Divides model parameters into fixed and adaptive parts, learning them separately offline and online.
result Significant improvement in AUC (2.48% absolute improvement).
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.
WCF uses Wasserstein distance to recommend cold-start items based on content similarity.
problem Recommendation performance drops for new items with little interaction history.
method Applies Wasserstein distance to map interaction history to contents, inferring user preferences.
result WCF outperforms state-of-the-art methods in cold-start recommendation.
Paper proposes a combined model for better recommendation by integrating explicit and implicit feedbacks.
problem Improve recommendation accuracy by considering both explicit and implicit feedbacks.
method Developed three models (RHC-PMF, RV-PMF, RHCV-PMF) that incorporate users' explicit and implicit feedbacks for better rating prediction.
result RHCV-PMF model outperforms other models in cold start scenarios for both users and items.
FAB-COST improves cold-start recommendation accuracy with less data.
problem Cold-start problem in recommendation systems.
method Contextual bandit algorithm using Expectation Propagation and Assumed Density Filtering.
result FAB-COST outperforms Laplace approximation on real data.
FBSM improves item recommendation for cold-start users by modeling feature interactions.
problem Cold-start item recommendation for new users.
method Factorized bilinear similarity model learning interactions among item features.
result Improves TOP-n recommendation performance compared to traditional methods.
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 …
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.
CHAMELEON tackles news recommendation using deep learning, addressing cold-start issues.
problem Personalizing user experiences in a dynamic news search space.
method Modular reference architecture with different neural building blocks, leveraging user and article context, and modeling temporal decay and concept drift.
result CHAMELEON outperforms traditional and state-of-the-art session-based recommendation algorithms in accuracy, item coverage, novelty, and reduced item cold-start problem.
New method for cold-start users in content recommendation systems.
problem Handling new users in content recommendation systems.
method Formulates an optimization problem to maximize session length, using MDP with greedy value iteration.
result Proves the problem has monotone and submodular properties, enabling efficient solution.
NetDP predicts loan defaults using network data, addressing cold-start issues.
problem Cold-start problem in default prediction for new users.
method Combines unsupervised and supervised network representations, using parameter-server for scalability.
result Effectiveness in cold-start problem, especially for new users.
Unified approach for conversational recommendation by integrating attributes and items.
problem Cold-start users' real-time personalization in online recommendation.
method Seamlessly unifies attributes and items in Thompson Sampling framework for interactive decision-making.
result Conversational Thompson Sampling (ConTS) outperforms existing methods in success rate and conversation turns.
SAIN integrates user-item feedback with content attributes for better recommendation.
problem Cold start problems in recommendation models due to sparse user-item interactions.
method SAIN uses a self-attention mechanism to capture feature interactions and an information integration layer to combine feedback and content information.
result SAIN outperforms state-of-the-art models by 2.13% on public datasets.
Study on online CF with occasional positive ratings, finds p_f impacts sample complexity.
problem Online CF with limited positive feedback.
method Probabilistic user model, online user-based CF algorithm analysis.
result Sample complexity reduced by 1/p_f for initial exploration.
New model predicts user ratings using chains of evidence.
problem Predicting user ratings from sparse data.
method Recursive evidence chains linking users and items.
result Competitive results in accuracy and speed.
JoVA combines two VAEs to learn user and item representations for better recommendation.
problem Collaborative filtering with implicit feedback.
method Joint Variational Autoencoders (JoVA) with a hinge-based pairwise loss function (JoVA-Hinge).
result JoVA-Hinge outperforms state-of-the-art methods in top-k recommendation.
The study proposes an audit to assess user control over recommendations in collaborative filtering systems.
problem The gap between maximizing accuracy and ensuring user control over information availability in recommender systems.
method The approach involves a computationally efficient audit for top-N linear recommender models, focusing on reachability and user agency. result The study demonstrates that model complexity affects the effort required for users to exert control over their recommendations.
CAB algorithm clusters users for better recommendation performance.
problem Cold-start problem in recommendation systems.
method Context-aware clustering of Bandits (CAB) with exploration-exploitation strategies.
result Significant increased prediction performance compared to state-of-the-art methods.
Survey of deep learning methods for recommender systems challenges.
problem Cold start and candidate generation challenges in recommender systems.
method Taxonomy of deep learning techniques addressing cold start and candidate generation problems.
result Taxonomy of deep learning techniques to address recommender systems challenges.
Deep learning from manga posters improves anime and manga recommendations.
problem Predicting user preferences for cold-start anime and manga recommendations.
method BALSE model using Illustration2Vec to extract tag information from posters.
result BALSE model significantly improves recommendation quality, especially for less-known mangas.
We address the cold start problem in recommendation systems assuming no contextual information is available neither about users, nor items. We consider the case in which we only have access to a set of ratings of items by users. Most of the existing works consider a batch setting, and use cross-validation to tune param…
SeER hybrid model improves song recommendations and explains them.
problem Improving song recommendations and explaining them.
method Collaborative filtering and deep learning sequence models on MIDI content.
result Personalized explanations capture user preferences.
Proposes a new graph-based model for cold-start recommendations.
problem Cold-start problem in Recommender Systems, especially for items with no interaction history.
method Graph-based feature weighting model using probabilities from item-based collaborative filtering.
result Shows improved recommendation quality compared to existing methods.
HybridSVD combines user and item info for efficient, flexible recommendations.
problem Lack of effective methods for incorporating both user and item side information in collaborative filtering.
method Hybrid algorithm using PureSVD with generalized singular value decomposition and cold start solution.
result Superior performance compared to similar hybrid models on various datasets.
LRMM learns to recommend with missing modalities, improving robustness to data sparsity and cold-start issues.
problem Learning to recommend with missing modalities and cold-start problems.
method LRMM uses modality dropout and multimodal sequential autoencoder to learn multimodal representations and impute missing modalities.
result LRMM achieves state-of-the-art performance on rating prediction tasks and is more robust to data sparsity and cold-start issues.
RippleNet uses a knowledge graph to improve recommendation by propagating user preferences.
problem Collaborative filtering sparsity and cold start problem.
method End-to-end framework that propagates user preferences over the knowledge graph.
result Ripple Network achieves substantial gains in recommendation performance.
Paper tackles cold start problem in recommendation systems using deep learning and latent factor models.
problem Cold start problem in recommendation systems for new users and items.
method Uses model-based approach and deep learning, specifically latent factor model and convolutional neural network.
result Significantly outperforms baseline estimators in experiments.
Federated multi-view matrix factorization learns from multiple data sources without centralizing user data.
problem Cold-start federated recommendations and multi-view data structure.
method Federated learning framework extended to multi-view matrix factorization.
result Federated multi-view matrix factorization outperforms simpler methods in cold-start federated recommendations.
Survey on using knowledge graphs for better recommender systems.
problem Data sparsity and cold start issues in recommender systems.
method Utilizes knowledge graphs to improve recommendation accuracy and provide explanations.
result Advantages of knowledge graph-based recommender systems.
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.
KGNN-LS improves recommender systems using knowledge graphs and label smoothness.
problem Improving recommender systems through better user-item embeddings.
method KGNN-LS combines knowledge graphs, user-specific embeddings, and label smoothness regularization.
result KGNN-LS outperforms state-of-the-art baselines and handles cold-start scenarios.
STAR-GCN improves recommender systems by learning node representations.
problem Cold start problem in recommender systems.
method Stacked and reconstructed Graph Convolutional Networks (GCN) with intermediate supervision and node embedding reconstruction.
result Significant improvements in predicting ratings, especially in the cold start scenario.
Hotel2vec learns hotel embeddings from multiple data sources.
problem Cold-start problem for hotels with insufficient click data.
method Self-supervised neural network architecture combining user clicks, hotel attributes, and geographic info.
result Improved downstream task predictions with structured hotel attributes.
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…
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.
KGCN uses KGs to improve recommender systems by connecting user and item attributes.
problem Collaborative filtering sparsity and cold start issues.
method End-to-end framework that captures inter-item relatedness using KGs and minibatch sampling.
result KGCN outperforms strong recommender baselines on movie, book, and music recommendation datasets.
A deep learning architecture for news session-based recommendations.
problem Challenges in news recommendation systems, including sparse user profiling and dynamic user preferences.
method Hybrid approach combining text and metadata features, session-based recommendations with Recurrent Neural Networks, and temporal offline evaluation.
result Significant improvement in top-n accuracy and ranking metrics (10% Hit Rate and 13% MRR) over best benchmark methods.
The paper tackles the issue of preferential attachment in targeted display advertising by developing domain-adaptation approaches.
problem Skewed distribution of data leads to preferential attachment towards high-budget partners.
method Develops domain-adaptation approaches to predict interested users for low-budget partners.
result Proposed approaches outperform other domain-adaptation methods across different points of campaigns.
Develops LLFR for efficient top-N recommendation in big data.
problem Efficiently producing personalized recommendations in big data.
method Lanczos-based low-dimensional item similarity model for top-N recommendation.
result LLFR outperforms state-of-the-art methods in computational and qualitative aspects.
Proposes a method to improve recommendation systems by considering negative feedback.
problem Insensitivity to negative feedback in top-N recommendation tasks.
method Model user feedback as a ternary variable and use third-order tensor factorization.
result Achieves state-of-the-art quality and significantly outperforms other methods in cold-start scenarios.
DiffQue estimates relative difficulty of questions in CQA services.
problem Estimating relative difficulty of questions in community Q&A services.
method Network-aided edge directionality prediction.
result DiffQue outperforms state-of-the-art methods by significant margins.
Study active learning for multi-level user preferences in recommendation systems.
problem Efficiently learning user preferences through active querying in recommendation systems.
method Proposes a theoretically optimal active learning strategy based on Fisher information matrix for collective matrix factorization.
result Demonstrates strong improvements over active learning methods in personalized, cold-start, and noisy data settings.
SPARC tackles cold-start nodes in graphs by using spectral embeddings.
problem Cold-start nodes in graphs lacking initial connections.
method Introduces SPARC, a framework utilizing spectral embeddings to predict on cold-start nodes.
result SPARC outperforms existing models on cold-start nodes across tasks.
FairJudge identifies fraudulent users in rating platforms.
problem Untrustworthy users giving fraudulent ratings.
method Three metrics: fairness, reliability, and goodness; iterative algorithm to predict these metrics.
result Significantly outperforms existing algorithms in predicting fair and unfair users.