Eigenvalue analogy explains item-based recommender system accuracy.
problem Lack of theoretical explanation for item-based recommender system success.
method Formalized as an eigenvalue problem, estimating ratings as true ratings multiplied by user-specific eigenvalues.
result Eigenvalue magnitude correlates with user's recommendation accuracy and can measure confidence.
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.
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.
Next basket recommendation improved with context-aware item representations.
problem Predicting users' next purchases based on historical transactions.
method Pre-trained context-aware item representations using transformers.
result IERT outperforms state-of-the-art methods in next basket prediction.
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.
Advances citation and subject label recommendation using multi-modal adversarial autoencoders.
problem Improving recommendation systems for citations and subject labels.
method Multi-modal adversarial autoencoders with adversarial regularization, sparsity, and input modality analysis.
result Adversarial regularization consistently improves recommendation performance.
New algorithm reduces regret in fatigue-aware online recommendation.
problem Fatigue leads to user abandonment in online recommendation systems.
method Thompson Sampling for combinatorial bandits with fatigue model.
result Polynomial regret bound in number of items, outperforming naive approaches.
In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i.e., number of seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the prevalence of the task of recommending items under capacity constraints in a variety of s…
The paper improves recommendation models by considering user interactions with recommended items.
problem Improving next item prediction in recommendation systems.
method Extending RNN framework with a recommendation action module and state-action fusion module.
result Improved performance on next item prediction compared to baselines.
The paper analyzes regret in online recommendation systems with constraints.
problem Analyzing regret in online recommendation systems with user-item constraints.
method Theoretical analysis and algorithm design considering user-item constraints and unknown probabilities.
result Derives regret lower bounds and algorithms achieving these limits for various structural assumptions.
Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time). In this pa…
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.
XploVAE improves recommendation by balancing known and novel items.
problem Balancing known and novel items for better recommendations.
method Constructs user-specific subgraphs for exploitation and exploration, learns personalized item embeddings.
result Demonstrates effectiveness on various 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%.
Debias recommender systems by accounting for hidden confounders using network information.
problem Debiased recommender systems to reduce bias caused by hidden confounders.
method Leverage network information to disentangle user conformity and item popularity, modeling exposure and ratings while controlling hidden confounders.
result The proposed method effectively debiases recommender systems, improving recommendation accuracy.
RecoBERT uses a language model to recommend items from catalogs.
problem Harnessing language models for text-based item recommendations.
method RecoBERT is a BERT-based approach that learns specialized language models for item recommendations without requiring labeled data.
result RecoBERT outperforms other techniques in inferring item similarities from textual catalogs.
We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. Each user may be recommended a given item at most once. A latent variable model specifies the user preferences: both users and items are clustered into types. Al…
In this paper, we consider decentralized sequential decision making in distributed online recommender systems, where items are recommended to users based on their search query as well as their specific background including history of bought items, gender and age, all of which comprise the context information of the use…
Etsy uses novel embeddings to improve user recommendations based on item interactions.
problem Improving personalized recommendations for users based on diverse item interactions.
method Learning interaction-based item embeddings to encode co-occurrence patterns of item and interaction types.
result Taking interaction type into account improves user shopping behavior modeling accuracy.
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.
Data poisoning attacks can manipulate recommender systems to recommend target items.
problem Attacks on recommender systems to influence top-N item recommendations.
method Formulated as an optimization problem, solved using influence function to select influential users.
result Effective data poisoning attacks that outperform existing methods.
Proposes SSL framework for improving item recommendations in large-scale systems.
problem Sparse feedback data for long-tail items in recommender systems.
method Multi-task self-supervised learning framework with data augmentation.
result Significant improvements in model performance, especially on slices lacking supervision.
Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant…
Despite the prevalence of collaborative filtering in recommendation systems, there has been little theoretical development on why and how well it works, especially in the "online" setting, where items are recommended to users over time. We address this theoretical gap by introducing a model for online recommendation sy…
A new dataset tracks user interactions and click responses in online marketplaces.
problem Lack of exposure data in recommender systems datasets.
method Proposes a novel dataset including slates and click responses, allowing more accurate likelihood models.
result Models using exposure data show more natural likelihood, reducing bias towards previously exposed items.
A new algorithm improves top-k recommendation accuracy by considering item payoffs uncertainty.
problem Suboptimal performance in top-k recommendation rankings due to varying item payoffs. method Proposes a risk-seeking utility function for ranking items based on estimated preference scores.
result Risk-seeking ranking yields the best performance in top-k recommendations. Proposes CF-SFL to improve sparse data recommendation.
problem Poor performance of CF in sparse data.
method Generative user feedback loop to simulate user feedback.
result Improves recommendation results on multiple datasets.
Develops M2 model for next-basket recommendation considering user preferences, item popularity, and transition patterns.
problem Next-basket recommendation problem considering user preferences, item popularity, and transition patterns.
method Mixed model with preferences, popularities, and transitions (M2) using ed-Trans for transition patterns among items.
result Significantly outperforms state-of-the-art methods on all datasets in all tasks, with up to 22.1% improvement.
A new CNN model predicts next items in user sessions.
problem Modeling long-range dependencies in item sequences is challenging.
method Introduced a simple yet effective generative model with a stacked 'holed' convolutional layer architecture and residual blocks.
result The model achieves state-of-the-art accuracy with less training time.
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.
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.
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.
We introduce normalized nonnegative models (NNM) for explorative data analysis. NNMs are partial convexifications of models from probability theory. We demonstrate their value at the example of item recommendation. We show that NNM-based recommender systems satisfy three criteria that all recommender systems should ide…
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.
JIMA uses multi-level preference data to recommend composite items.
problem Recommending composite items efficiently with multi-level preference information.
method Joint Interaction Modeling (JIMA) approach that integrates multi-level preference data and interactions.
result JIMA outperforms advanced baselines in offline and online settings.
This paper proposes a framework to learn explainable rules from knowledge graphs for better recommendation.
problem Combining side information with explainability in recommendation systems.
method Joint learning framework integrating rule induction from knowledge graphs with a rule-guided neural recommendation model.
result Significant improvements in item recommendation performance over baselines.
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.
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.
The paper examines the consistency of item embeddings in recommendation systems.
problem The relevance of averaging item embeddings for user or concept representation.
method Proposes an expected precision score to measure consistency and analyzes it theoretically and empirically.
result Real-world averages are less consistent for recommendation compared to theoretical assumptions.
SR-GNN models sessions as graphs to predict user actions.
problem Predict user actions based on anonymous sessions.
method SR-GNN models sessions as graphs and uses attention networks.
result SR-GNN outperforms state-of-the-art methods consistently.
Improves search performance by transferring knowledge from recommender system.
problem Cold start and feedback loop problems in search retrieval.
method Zero-Shot Heterogeneous Transfer Learning framework.
result Significant improvements in relevance and user interactions over production system.
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.
Algorithm learns diverse recommendations from partial clicks.
problem Learning diverse recommendations from partial user feedback.
method Online learning algorithm for cascade model.
result Gap-free upper bound on n-step regret proved.
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.
A new method for multi-criteria recommender systems using graph attention networks.
problem Lack of nuanced relationships between users and items based on specific criteria.
method MDGAT, a multi-edge bipartite graph with dual attention networks and contrastive learning.
result MDGAT achieves higher accuracy in predicting item ratings compared to baseline 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.
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressi…
A new multi-task framework for recommender systems improves ranking and rating predictions.
problem Improving ranking and rating predictions in recommender systems.
method Exploits a two-phase decision process: first deciding to interact with an item (ranking task) and then rating it (rating prediction task).
result Superior performance compared to state-of-the-art methods on two benchmark datasets.