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Found 10 papers, 1 papers with code
Optimizing Airbnb Search Journey with Multi-task Learning
The long and exploratory nature of the search journey, as well as the need to balance both guest and host preferences, present unique challenges for Airbnb search ranking.
Learning To Rank Diversely At Airbnb
Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results.
Deep Natural Language Processing for LinkedIn Search
Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help.
Incremental Learning for Personalized Recommender Systems
Ubiquitous personalized recommender systems are built to achieve two seemingly conflicting goals, to serve high quality content tailored to individual user's taste and to adapt quickly to the ever changing environment.
Deep Natural Language Processing for LinkedIn Search Systems
Many search systems work with large amounts of natural language data, e. g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help.
Deep Search Query Intent Understanding
Understanding a user's query intent behind a search is critical for modern search engine success.
Efficient Neural Query Auto Completion
On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin.
DeText: A Deep Text Ranking Framework with BERT
Ranking is the most important component in a search system.
Memory-efficient Embedding for Recommendations
Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.
mTrust: Discerning Multi-Faceted Trust in a Connected World
Traditionally, research about trust assumes a single type of trust between users.
