In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new ite…
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48 results for “Item Attributes”
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.
This paper surveys and classifies attribute-aware CF models.
problem Rating prediction with user and item attributes.
method Mathematical classification of attribute-aware CF models into four categories.
result Comprehensive comparison of effectiveness among different categories.
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.
Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to c…
New framework for fair ranking with noisy protected attributes.
problem Errors in socially-salient attributes undermine fairness guarantees.
method Modeling perturbations in protected attributes and incorporating probabilistic information.
result Framework provides provable guarantees on fairness and utility.
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.
Paper learns latent and hierarchical structures in CDMs from data.
problem Jointly learning latent and hierarchical structures in CDMs from observed data.
method Penalized likelihood approach for selecting attributes and estimating structures; EM and latent structure recovery algorithms.
result Good performance demonstrated by simulation and real data applications.
Paper tackles embedding attributed sequences in unsupervised learning.
problem Mining tasks over attributed sequences with dependencies between sequences and attributes.
method Proposes a deep multimodal learning framework, NAS, for unsupervised learning of attributed sequences.
result NAS produces task-independent embeddings for various mining tasks on real-world 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.
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.
Clustered Mallows Modelstat.ME
Extends Mallows model to handle item indifference in rankings.
problem Real data often contains item indifference, challenging strict preference assumptions.
method Proposes Clustered Mallows Model (CMM) to accommodate item indifference.
result CMM provides a flexible representation of rank collections with ordered clusters.
Method retrieves similar fashion items from images and text, enabling style refinement.
problem Lack of intuitive, interactive refinement in search engines for fashion items.
method Joint visual-textual embedding training, Mini-Batch Match Retrieval, attribute extraction.
result Improved performance in multimodal style search, demonstrated through benchmark.
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.
Novel method diagnoses large language models' reasoning abilities.
problem Fine-grained evaluation of large language models' reasoning abilities.
method Adapting cognitive diagnosis models to LLMs, estimating mastery profiles and Q-matrix, incorporating textual information.
result Accurate parameter recovery and insights into LLMs' capabilities.
Outfittery uses machine learning to help stylists choose appropriate fashion items.
problem Selecting appropriate fashion items and ensuring relevance to customers.
method Combining machine learning with human expertise to recommend items by style fit and relevance.
result The method successfully recommends fashion items by style fit and relevance.
ESRLCM clusters similar responses, more broadly than traditional models.
problem Clustering multivariate categorical data with common response patterns.
method Bayesian Equivalence Set Restricted Latent Class Model (ESRLCM).
result ESRLCM identifies clusters with similar item response probabilities.
Calendar graph neural networks model user behavior with location and time data.
problem Modeling user behavior with location and time information for demographic prediction.
method Graph neural networks with a tripartite network of items, sessions, and locations, and a hierarchical calendar network.
result User embeddings preserve spatial and temporal patterns of various periodicity.
WGNN learns graph representations from incomplete attribute data.
problem Missing node attributes in graphs.
method WGNN learns node representations from decomposed attribute matrices and uses Wasserstein space for message passing.
result WGNN outperforms existing methods in node classification tasks with missing attribute data.
The paper develops a method to estimate consumer preferences from observed rankings.
problem Estimating consumer preferences from partial ranking information.
method Interpreting observed rankings as pairwise comparisons, modeling latent utility, and correcting for selection bias.
result The method improves recommendation performance, especially for previously unconsumed products.
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.
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.
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Predictioncs.LG
New method interprets deep neural networks for better recommendation system understanding.
problem Making deep neural networks explainable for better user trust and understanding.
method Proposes a novel formulation of interpretable deep neural networks using masked weights and hidden features.
result Demonstrates models achieving close predictive performance with informative attributions.
SAGE-FIN detects financial fraud using GNNs and Granger causality.
problem Detecting fraud in financial networks with limited labeled data and lack of explainability.
method Semi-supervised GNN approach with Granger causal explanations.
result SAGE-FIN outperforms on real-world financial network dataset with explainable flagged items.
A framework uses preprocessing to improve psychiatric questionnaire predictions while maintaining interpretability.
problem Weak predictive accuracy and limited interpretability of psychiatric questionnaires.
method Two-stage method: stable preprocessing followed by a linear mapping.
result REFINE outperforms other interpretable approaches in psychiatric and non-psychiatric prediction tasks.
We consider the problem of segmenting a large population of customers into non-overlapping groups with similar preferences, using diverse preference observations such as purchases, ratings, clicks, etc. over subsets of items. We focus on the setting where the universe of items is large (ranging from thousands to millio…
Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. This gap bet…
Proposes a model to incentivize exploration in web platforms with payments.
problem Learning and social welfare goals of web platforms with myopic users.
method Contextual bandit model with payments to incentivize exploration.
result Achieves sublinear regret while maximizing cumulative social welfare.
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existi…
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.
Quantification is a supervised learning task that consists in predicting, given a set of classes C and a set D of unlabelled items, the prevalence (or relative frequency) p(c|D) of each class c in C. Quantification can in principle be solved by classifying all the unlabelled items and counting how many of them have bee…
Recommendation algorithms are widely adopted in marketplaces to help users find the items they are looking for. The sparsity of the items by user matrix and the cold-start issue in marketplaces pose challenges for the off-the-shelf matrix factorization based recommender systems. To understand user intent and tailor rec…
Training data attribution in diffusion models via mirrored unlearning and noise-consistent skewcs.LG
Paper proposes MUCS for more reliable TDA in diffusion models.
problem Current TDA approaches lack reliability and robustness.
method Mirrored unlearning and noise-consistent skew (MUCS).
result MUCS outperforms existing methods on three datasets.
Introduces MPR to measure and optimize representation across intersectional groups in retrieval.
problem Harmful stereotypes, cultural erasure, and social disparities in image search and retrieval.
method Develops MPR metric, practical estimation methods, theoretical guarantees, and optimization algorithms.
result Optimizing MPR yields more proportional representation across multiple intersectional groups, often with minimal retrieval accuracy compromise.
Framework for optimizing search engine rankings using observational data.
problem Optimizing ranking policies for search engines using limited observational data.
method Formulated expected reward optimization problem, estimated context value distribution, trained ranking policy via Bayesian inference.
result Demonstrated trade-offs in ranking policies trained on empirical reward estimates.
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.
New ranking system balances fairness and user utility.
problem Achieving group fairness in ranking systems.
method Formulated a minimax game between a ranking player and an adversary.
result Better utility for highly fair rankings.
This thesis explores supervised classification methods using Bayesian and exchangeability theories.
problem Assigning objects into predefined classes using training data and auxiliary information.
method Bayesian inductive theories and exchangeabilities (de Finetti and partition exchangeability).
result Optimal classifiers for different scenarios of object features and categories.
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.
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 proposes CARE model for ranking with covariates, improving MLE accuracy.
problem Statistical estimation and inference for ranking with covariate information.
method Covariate-Assisted Ranking Estimation (CARE) model, extending Bradley-Terry-Luce (BTL) model.
result Derives optimal rates and asymptotic distributions for MLE of latent scores and covariates.
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.
Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studiescs.CY
A personalized learning system needs a large pool of items for learners to solve. When working with a large pool of items, it is useful to measure the similarity of items. We outline a general approach to measuring the similarity of items and discuss specific measures for items used in introductory programming. Evaluat…
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.