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Found 7 papers, 1 papers with code
Position Bias Estimation with Item Embedding for Sparse Dataset
Using a public dataset and internal carousel advertisement click dataset, we empirically show that item embedding with Latent Semantic Indexing (LSI) and Variational Auto-Encoder (VAE) improves the accuracy of position bias estimation and the estimated position bias enhances Learning to Rank performance.
Exploring 360-Degree View of Customers for Lookalike Modeling
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base.
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation
We first show transferring knowledge and incorporating temporal dynamics improve the performance of the baseline models on a synthetic dataset.
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation
The smoothing is specially desired in the presence of homophilic graphs, such as the ones we find on recommender systems.
Character 3-gram Mover's Distance: An Effective Method for Detecting Near-duplicate Japanese-language Recipes
In user-generated recipe websites, users post their-original recipes.
Learning Classifiers on Positive and Unlabeled Data with Policy Gradient
Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a classification model.
Deep Heterogeneous Autoencoders for Collaborative Filtering
This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems.
