Search Results for author:
Found 9 papers, 3 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.
Meta-Shop: Improving Item Advertisement For Small Businesses
Training samples in RS can be highly biased toward popular businesses with sufficient sales and can decrease advertising performance for small businesses.
One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations
In one-class recommendation systems, the goal is to learn a model from a small set of interacted users and items and then identify the positively-related user-item pairs among a large number of pairs with unknown interactions.
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.
Learning Similarity Preserving Binary Codes for Recommender Systems
We conducted experiments on MovieLens1M, Amazon product review, Ichiba purchase dataset and confirmed CCSR outperformed the existing matrix factorization-based methods.
Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering
Then, we propose to learn the value of the non-zero elements of the inputs jointly with the neural network parameters.
Efficient Cross-Modal Retrieval via Deep Binary Hashing and Quantization
We simultaneously learn binary hash codes and quantization codes to preserve semantic information in multiple modalities by an end-to-end deep learning architecture.
Recommending Short-lived Dynamic Packages for Golf Booking Services
We introduce an approach to recommending short-lived dynamic packages for golf booking services.
Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization
We also apply the NRP framework to a direct neural network structure which predicts the ratings without reconstructing the user and item information.
