Search Results for author:
Found 9 papers, 4 papers with code
Realtime query completion via deep language models
Search engine users nowadays heavily depend on query completion and correction to shape their queries.
THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS
We present a personalized recommender system using neural network for recommending products, such as eBooks, audio-books, Mobile Apps, Video and Music.
Adaptive, Personalized Diversity for Visual Discovery
Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired.
Compressing Gradient Optimizers via Count-Sketches
The problem is becoming more severe as deep learning models continue to grow larger in order to learn from complex, large-scale datasets.
Semantic Product Search
To address these issues, we train a deep learning model for semantic matching using customer behavior data.
Leverage Implicit Feedback for Context-aware Product Search
However, customers with little or no purchase history do not benefit from personalized product search.
A Study of Context Dependencies in Multi-page Product Search
In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search.
RAMBO: Repeated And Merged Bloom Filter for Multiple Set Membership Testing (MSMT) in Sub-linear time
A simple array of Bloom Filters can achieve that.
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
Our largest model has 6. 4 billion parameters and trains in less than 35 hours on a single p3. 16x machine.
