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Found 8 papers, 5 papers with code
Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating
To estimate the user-bundle relationship more accurately, CoHeat addresses the highly skewed distribution of bundle interactions through a popularity-based coalescence approach, which incorporates historical and affiliation information based on the bundle's popularity.
Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation
However, it is challenging to tackle aggregate-level diversity with a matrix factorization (MF), one of the most common recommendation model, since skewed real world data lead to skewed recommendation results of MF.
Accurate Bundle Matching and Generation via Multitask Learning with Partially Shared Parameters
BundleMage effectively mixes user preferences of items and bundles using an adaptive gate technique to achieve high accuracy for the bundle matching.
Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge
SmartSense then summarizes sequences of users considering queried contexts in a query-attentive manner to extract the query-related patterns from the sequential actions.
Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
Given a graph with partial observations of node features, how can we estimate the missing features accurately?
Multi-EPL: Accurate Multi-source Domain Adaptation
Given multiple source datasets with labels, how can we train a target model with no labeled data?
Ensemble Multi-Source Domain Adaptation with Pseudolabels
Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels.
Data Context Adaptation for Accurate Recommendation with Additional Information
Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities?
