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.
Proposes MCCF to distinguish latent purchasing motivations in user-item interactions.
problem Difficulty in capturing fine-grained user preferences due to complex latent motivations.
method Introduces MCCF with decomposer and combiner modules to identify and recombine latent components.
result Significant performance gains and necessity of considering multiple components demonstrated.
TransCF improves recommendation by modeling user-item relationships with translation vectors.
problem Triangle inequality violation in matrix factorization-based recommendation methods.
method TransCF uses translation vectors to model latent user-item relationships in implicit feedback.
result TransCF outperforms state-of-the-art methods by up to 17% in hit ratio.
JODIE learns dynamic user-item embeddings from interactions, outperforming existing methods.
problem Modeling dynamic user-item interactions for accurate future predictions.
method JODIE uses coupled recurrent models with update, projection, and prediction components, and a novel t-Batch algorithm.
result JODIE outperforms state-of-the-art methods by up to 22.4% on future interaction and state change prediction tasks.
New neural network predicts user-item relationships in evolving graphs.
problem Link prediction in dynamic graphs for recommendation services.
method Proposes a new neural network approach to leverage temporal contextual information.
result Our approach produces better predictions in scenarios with changing user-item relationships.
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.
Improved job recommendations using temporal learning and sequence modeling.
problem Enhancing job recommendation accuracy through complex user-item activity patterns.
method Combining time-based ranking with hybrid matrix factorization and RNN for sequence modeling.
result RNN-based model achieved 5th place in RecSys Challenge 2016.
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.
Expands small recommendation datasets to industrial scale.
problem Disconnection between academic and industrial data scales.
method Randomized fractal expansions using Kronecker Graph Theory.
result Generated synthetic data sets with 1.2B ratings, 2.2M users, and 855K items.
PGRec improves recommendation by modeling user-item preferences as a graph and embedding it for better predictions.
problem Sparse user-item data in recommender systems.
method PGRec models user-item preferences as a PrefGraph, then uses deep learning and factorization to embed and predict user preferences.
result PGRec outperforms state-of-the-art methods by up to 3.2% in NDCG@10.
The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.
problem High dimensionality and sparsity in user-item data, especially in terms of revenue contributions.
method The approach encodes revenue contributions in the user-item matrix and computes customer similarity using suitable distance measures.
result The method segments users based on revenue-based similarity and supports recommendations aligned with profitability objectives.
Enhances clustering of user-item interactions using item content.
problem Lack of item content in traditional clustering methods.
method Content-Augmented Stochastic Blockmodels (CASB).
result CASB provides highly accurate clusters with respect to community structure metrics.
Matrix factorization simplifies user-item co-occurrence analysis.
problem Understanding the meaning of low-dimensional matrices in matrix factorization.
method Showed matrix factorization equals calculating eigenvectors of co-occurrence matrices, using RMT insights.
result Low-dimension matrices represent a reduced noise user and item co-occurrence space.
Optimizes recommendation rankings using AUC for practical user-item presentation.
problem Optimizing recommendation system rankings for practical user-item presentation.
method Proposes a class of smooth surrogate objective functions over matrix factorizations for AUC maximization, with a stochastic gradient descent algorithm.
result Shows improved computational efficiency and theoretical consistency for AUC maximization.
A new linear GCN model improves recommendation performance for large graphs.
problem Training difficulties and over-smoothing in GCN-based CF models.
method Proposes a linear residual graph convolutional network (LRGCCF) to address training difficulties and over-smoothing issues.
result The proposed model yields better efficiency and effectiveness on real datasets.
Generates massive synthetic data sets for recommender systems.
problem Size gap between academic data sets and industrial production systems.
method Expands pre-existing public data sets using Kronecker Graph Theory.
result Preserves higher order statistical properties of user/item interactions.
Geometric matrix completion learns graph patterns and non-linear diffusion efficiently.
problem Efficiently learn graph patterns and non-linear diffusion from user/item graphs.
method Geometric deep learning on graphs with graph convolutional and recurrent neural networks.
result Outperforms state-of-the-art techniques on synthetic and real datasets.
GEMRank embeds users and items using co-occurrence relations for better collaborative filtering.
problem Lack of textual data for entity embedding in recommender systems.
method Uses profile co-occurrence for entity relations and factorization for embedding. Feeds embeddings into a neural network for predictions.
result Significantly outperforms baseline algorithms in various data sets.
Proposes a tree-based method to efficiently predict user interests in large recommender systems.
problem Efficiently predicting user-item preferences in large recommender systems with high calculation costs.
method Predicts user interests from coarse to fine using a tree structure, which can incorporate deep neural networks.
result Significantly outperforms traditional methods in both training and prediction.
New methods improve recommendation accuracy for users and items with few ratings.
problem Skewed distribution and low ratings affect recommendation accuracy.
method Four matrix completion-based approaches: FARP, TMF, TMF + Dropout, IFWMF.
result Improved prediction accuracy for users and items with few ratings.
Graph auto-encoder predicts user-item interactions from graph data.
problem Matrix completion for recommender systems from graph data.
method Differentiable message passing on bipartite graphs.
result Competitive performance on collaborative filtering benchmarks.
Improved neural model for social recommendation by integrating social and interest networks.
problem Data sparsity and lack of higher-order relationships in social recommendation.
method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.
result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.
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.
This paper tackles selection bias in recommender systems by considering the neighborhood effect.
problem Selection bias in recommender systems due to filtering and user selection.
method Formalizes neighborhood effect as interference problem, introduces treatment representation, and proposes ideal loss.
result Proposed methods achieve unbiased learning when both selection bias and neighborhood effect are present.
Proposes a multilayer nonlinear semi-nonnegative matrix factorization for better recommendation.
problem Inaccurate user-item interaction modeling with classical matrix factorization.
method Multilayer nonlinear Semi-NMF approach for latent user and item representations.
result Proposed method achieves better generalization in prediction and comparable representation in clustering.
New method uses bandit feedback to better evaluate recommender systems.
problem Traditional offline evaluation of recommender systems is inaccurate.
method Exploits bandit feedback to estimate online performance.
result Bandit feedback provides more accurate offline evaluation.
KHGRec tackles noisy and incomplete KG-enhanced recommendations by modeling complex interactions.
problem Challenges in integrating KGs for accurate recommendations, especially in complex higher-order interactions and heterogeneous modalities.
method KHGRec uses a collaborative knowledge heterogeneous hypergraph (CKHG) to model group-wise interdependencies, employing two hypergraph encoders and attention mechanisms.
result KHGRec achieves an average 5.18% relative improvement over state-of-the-art baselines on four real-world datasets.
Improved item recommendations for repeat interactions using sequence analysis.
problem Limited effectiveness of traditional recommender systems in handling repeated user-item interactions.
method Designed a recommender system that considers sequences of item interactions for each user.
result Empirically shown to give highly accurate predictions and increase sales by 5%.
Proposes a graph neural network for personalized news recommendation.
problem Data sparsity in news recommendation systems.
method Heterogeneous graph model + Graph Neural Networks + LSTM attention mechanism.
result Significantly outperforms state-of-the-art methods on news recommendation datasets.
Hybrid Deep Embedding for aspect-level explanations in recommendations.
problem Challenges in personalization, dynamic explanations, and aspect-level granularity in recommendation systems.
method Proposes Hybrid Deep Embedding (HDE) to learn dynamic embeddings for user and item preferences, and aspect-level quality vectors.
result Demonstrates improved recommending performance and dynamic aspect-level explanations.
The paper proposes sampling strategies to speed up neural network-based recommendation algorithms.
problem High computational costs in neural network-based recommendation algorithms.
method Established a connection between loss functions and user-item interaction graph, proposed three novel sampling strategies.
result Significant improvement in training efficiency (up to 30 times speedup).
Deep Retrieval learns a retrievable structure for efficient large-scale recommendations.
problem Efficiently retrieving top relevant candidates in large-scale recommendation systems.
method Deep Retrieval learns a retrievable structure directly from user-item interaction data, encoding candidates into a discrete latent space and optimizing a model to maximize accuracy.
result Deep Retrieval achieves almost the same accuracy as brute-force baseline and significantly outperforms ANN baselines in a live production system.
Develops a two-level monotonic multistage recommender system for better user-specific prediction.
problem Leveraging user-item-stage dependencies in a monotonic chain of events for enhanced prediction accuracy.
method A multistage recommender system with a two-level monotonic property, using a large-margin classifier based on a nonnegative additive latent factor model.
result The proposed method outperforms existing methods in simulations and an article sharing dataset.
A new algorithm MBMF improves recommendation accuracy and speed for sparse datasets.
problem Sparse and fluctuating predictions in recommender systems.
method MBMF uses magnitude constraints and Spherical coordinates to optimize faster than existing methods.
result MBMF outperforms existing algorithms in accuracy and speed on synthetic and real datasets.
Improved Top-N recommendations with novel rank approximation.
problem Low accuracy in recommender systems.
method Linear sparse and low-rank representation with nuclear norm relaxation.
result Significantly improved Top-N recommendation accuracy.
We develop a Bayesian Poisson matrix factorization model for forming recommendations from sparse user behavior data. These data are large user/item matrices where each user has provided feedback on only a small subset of items, either explicitly (e.g., through star ratings) or implicitly (e.g., through views or purchas…
KGAT uses knowledge graphs to improve recommendation accuracy and explainability.
problem Accurate, diverse, and explainable recommendations require side information and collaborative signals.
method KGAT models high-order relations in a knowledge graph by propagating embeddings and using attention mechanisms.
result KGAT significantly outperforms state-of-the-art methods on public benchmarks.
A new method speeds up ALS for recommender systems by subsampling key elements.
problem High computational cost of ALS for large-scale datasets.
method Core-elements subsampling method for efficient ALS approximation.
result Achieves similar accuracy with significantly reduced computational time.
IGMC learns inductive matrix completion without side info.
problem Inductive matrix completion without side information.
method Graph Neural Network (GNN) trained on 1-hop subgraphs of the rating matrix.
result Achieves competitive performance with state-of-the-art transductive baselines.
HGP tackles noisy connections and heterogeneity in social graphs for better social recommendation.
problem Challenges in social graphs, especially noisy connections and heterogeneity, hinder GNN-based social recommendation.
method HGP uses a tripartite graph to reduce complexity, personalized PageRank for node embeddings, and attention mechanism for integration.
result HGP outperforms baselines in AUC and F1-score metrics on a large-scale dataset.
A method learns matrix factorization from diverse matrices and applies the knowledge to unseen matrices.
problem Matrix factorization without shared rows or columns.
method Neural network meta-learned to minimize expected imputation error using MAP estimation.
result The method can impute missing values from unseen matrices efficiently.
CSA improves recommender systems by learning context-aware feature representations.
problem Limited expressiveness of traditional IMC methods for feature representations.
method Generalizes self-attention mechanism to IMC, learning context-aware feature representations.
result Extensive experiments show CSA's effectiveness on real RS datasets.
CGM combines SSL and LFM for better recommendation performance.
problem Label sparsity in user-item rating matrices limits LFM performance.
method Probabilistic chain graph model (CGM) integrating Bayesian network and Markov random field.
result CGM significantly outperforms state-of-the-art approaches in recommendation.
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.
Dynamic model predicts user preferences over time.
problem Static user preferences in collaborative filtering.
method Compound Poisson Factorization with Gamma-Markov chains.
result DCPF achieves higher predictive accuracy than static models.
Secure social recommendation framework using secret sharing.
problem Privacy concerns and reluctance to share social data in recommender systems.
method Secret Sharing based Matrix Multiplication (SSMM) protocol for secure data sharing and collaborative recommendation.
result SeSoRec framework improves recommendation performance and is secure.
Integrates contextual constraints into embedding models for better recommendation quality.
problem Contextual constraints lead to incomplete or low-quality recommendations when applied independently.
method Merges constraint application and retrieval into one operation in the embedding space.
result Significant improvements in predictive performance compared to context-aware and standard models.
Deep factorization machines improved SLAM task performance.
problem Improving second language acquisition modeling.
method Used deep factorization machines to model pairwise relationships.
result Achieved AUC 0.815, beating logistic regression baseline.