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.
Extends recommender methods to respect capacity constraints.
problem Recommendation under capacity constraints in various settings.
method Extend three state-of-the-art latent factor recommendation approaches (PMF, GeoMF, BPR) to optimize for both recommendation accuracy and expected item usage that respects capacity constraints.
result Experimental results highlight the benefit of the method for recommendation under capacity constraints.
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 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.
Improved biclustering algorithm reduces memory usage and runtime.
problem Efficiently enumerating maximal biclusters in numerical datasets.
method Online partitioning to guide biclustering results.
result RIn-Close_CVC3 reduces memory usage and runtime, handles missing values.
DynAEsti models ability as a time-varying curve, improving IRT for dynamic settings.
problem Traditional IRT models assume static ability; new approach models dynamic ability.
method DynAEsti augments traditional IRT Expectation Maximization with CurvFiFE for curve fitting.
result DynAEsti successfully recovers human performance dynamics in golf.
This paper introduces new methods to improve 1-bit matrix completion by considering cluster effects.
problem Improving 1-bit matrix completion for clustered data.
method Group-Specific 1-bit Matrix Completion (GS1MC) and Cluster Developing Matrix Completion (CDMC).
result GS1MC and CDMC outperform existing methods in synthetic and real-world data.
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.
We link disjoint longitudinal data for rare disease patients using latent representations and mixed-effects regression.
problem Analyzing treatment switches in rare diseases with limited data and changing measurement instruments.
method We embed item values into a shared latent space using variational autoencoders and apply mixed-effects regression to quantify treatment effects.
result Our approach allows for statistical inference and quantifies the impact of treatment switches in spinal muscular atrophy.
EdgeLite detects hazardous supermarket floors, improving safety.
problem Detecting hazardous conditions on supermarket floors to prevent injuries.
method Developed a lightweight deep learning model, EdgeLite, for edge devices.
result EdgeLite outperformed state-of-the-art models in detecting hazards on supermarket floors.
NIS learns optimal embedding sizes for recommendation models.
problem Finding optimal embedding sizes for large-scale recommendation models.
method Neural Input Search (NIS) uses reinforcement learning to automatically find optimal vocabulary sizes and embedding dimensions.
result NIS improves prediction accuracy by 6.8% on Recall@1 and 1.8% on ROC-AUC.
Framework learns item representations from text data for complementary and similar items.
problem Generating accurate complementary item recommendations from textual data.
method Quadruplet network learning framework for latent space representation of items.
result Items are placed closer together in latent space for similar and complementary items compared to non-complementary items.
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.
Bayesian method improves adaptive testing item selection, ensuring full item exposure.
problem Adaptive testing selects items to estimate ability, but must also ensure diverse item exposure.
method Formulated as Bayesian model averaging, deriving optimal item sampling probabilities.
result Stochastic method achieves full item bank exposure without sacrificing accuracy.
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.
Model predicts cognitive health risks based on smartphone usage patterns.
problem Identifying cognitive health risks through smartphone usage.
method Structured models of smartphone interactions analyzed over 12 weeks.
result AUROC of 0.79 in discriminating between healthy and symptomatic subjects.
The paper analyzes and optimizes recommendation systems using user-user and item-item collaborative filtering.
problem Optimizing recommendation systems to minimize disliked recommendations.
method Proposes algorithms inspired by user-user and item-item collaborative filtering, proving performance guarantees in terms of expected regret.
result Information-theoretic lower bounds on regret match upper bounds up to logarithmic factors in two model parameter regimes.
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.
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.
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.
Active learning improves ordering of items with contextual attributes.
problem Learning accurate item orderings from pairwise comparisons, especially when exhaustive comparisons are impractical.
method Proposes an active learning strategy that samples items to minimize expected ordering error, accounting for uncertainty in comparisons.
result Superior sample efficiency and generalization compared to non-contextual ranking approaches and active preference learning baselines.
The study measures similarity in introductory programming items, offering a method and evaluation.
problem Measuring similarity in a diverse pool of programming items for personalized learning.
method General approach to measuring similarity, specific measures for introductory programming, three levels of abstraction evaluation.
result Evaluation of similarity measures using diverse programming environments.
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.
This paper optimizes the number of comparisons needed to find the best k items from pairwise comparisons.
problem Finding the best k items from pairwise comparisons with limited comparisons.
method Developed algorithms for finding probably approximately correct and exact best k items under stochastic conditions.
result Upper and lower bounds on the number of comparisons for finding the best k items, with matching upper bounds for PAC best k items.
Two methods improve 10-K item segmentation using large language models.
problem Challenges in extracting specific items from 10-K reports due to variations in document formats and item presentation.
method Two advanced item segmentation methods: GPT4ItemSeg and BERT4ItemSeg.
result BERT4ItemSeg achieves a macro-F1 of 0.9825, surpassing other methods.
A new algorithm estimates item parameters in item response theory models.
problem Estimating item parameters in item response theory models.
method Computation of the stationary distribution of a Markov chain defined on an item-item graph.
result Our algorithm is consistent and enjoys favorable optimality properties.
Paper introduces consumed item packs for better recommendation.
problem Personalizing web content using implicit feedback.
method Introduces consumed item packs (CIP) to link users/items based on consumption behavior.
result CIP-U, CIP-I, DEEPCIP, and FISM provide competitive recommendation quality.
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…
Algorithm clusters items by sequentially selecting features, minimizing observations.
problem Clustering items based on bandit feedback with many features.
method Sequential Halving algorithm for feature selection.
result Accurate recovery of item partition with minimal observations.
System segments Form 10-K documents into Item sections for financial analysis.
problem Segmenting Form 10-K documents into Item sections for efficient financial analysis.
method Developed an automatic Form 10-K Itemization system using NLP techniques.
result System achieves a retrieval rate of 93% for segmenting Item sections.
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.
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.
Algorithm identifies best item from subsets with random utility model feedback.
problem PAC learning the best item from subsets with random utility model feedback.
method Pairwise relative counts and hierarchical elimination for learning algorithm.
result Near-optimal PAC sample complexity guarantee for identifying ε-optimal item.
Adaptive cascade submodular maximization tackles sequential selection under uncertainty.
problem Maximizing expected utility from a set of items with unknown states and continuation probabilities.
method Proposed adaptive cascade submodular functions and a 0.12 approximation algorithm.
result Identified a class of functions (adaptive cascade submodular) that many practical applications satisfy.
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.
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 …
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.
New algorithms for fair item allocation with limited copies.
problem Fair division of numerous items with few copies.
method Modeling as a contextual bandit problem with sub-linear regret guarantees.
result Proposed algorithms achieve sub-linear regret in fair item allocation.
We consider the problem of learning soft assignments of N items to K categories given two sources of information: an item-category similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an item-item similarity mat…
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.
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.
New estimators improve Rasch model item parameter estimation for sparse data.
problem Estimating item parameters in sparse Rasch model data.
method Random pairing maximum likelihood estimator (RP-MLE) and its bootstrapped variant (MRP-MLE).
result RP-MLE and MRP-MLE are minimax optimal and provide precise item parameter estimates.
Study ranking in generalized linear bandits with position and item dependencies.
problem Complex reward function due to position and item dependencies in recommendation systems.
method Model position and item dependencies, design UCB and Thompson Sampling algorithms.
result Generalizes existing studies in position dependencies and graph theory.
Generative methods for creating new items for user groups.
problem Creating new items for groups of users with varying preferences.
method Formalized joint problem, used VAE latent space for item generation and user group prediction.
result Generated items similar to highly desirable unobserved items.
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.
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…
Gradient optimization improves preference elicitation for large item spaces.
problem Computational infeasibility of EVOI for large item spaces in recommender systems.
method Continuous formulation of EVOI as a differentiable network, optimized using gradient methods.
result Gradient-based EVOI optimization achieves state-of-the-art performance and scalability.