24 citations · 65 across the 10 of their papers we have counts for
10 papers
Optimizing Airbnb Search Journey with Multi-task Learning
Chun How Tan, Austin Chan, Malay Haldar +6
At Airbnb, an online marketplace for stays and experiences, guests often spend weeks exploring and comparing multiple items before making a final reservation request. Each reservat…
Learning To Rank Diversely At Airbnb
Malay Haldar, Mustafa Abdool, Liwei He +3
Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank…
Deep Natural Language Processing for LinkedIn Search
Weiwei Guo, Xiaowei Liu, Sida Wang +7
Many search systems work with large amounts of natural language data, e.g., search queries, user profiles, and documents. Building a successful search system requires a thorough un…
Incremental Learning for Personalized Recommender Systems
Yunbo Ouyang, Jun Shi, Haichao Wei +1
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 qui…
Deep Natural Language Processing for LinkedIn Search Systems
Weiwei Guo, Xiaowei Liu, Sida Wang +6
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 techn…
Deep Search Query Intent Understanding
Xiaowei Liu, Weiwei Guo, Huiji Gao +1
Understanding a user's query intent behind a search is critical for modern search engine success. Accurate query intent prediction allows the search engine to better serve the user…
Efficient Neural Query Auto Completion
Sida Wang, Weiwei Guo, Huiji Gao +1
Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to user experience. Generally it has two steps: generating completed query candidates…
DeText: A Deep Text Ranking Framework with BERT
Weiwei Guo, Xiaowei Liu, Sida Wang +8
Ranking is the most important component in a search system. Mostsearch systems deal with large amounts of natural language data,hence an effective ranking system requires a deep un…
Memory-efficient Embedding for Recommendations
Xiangyu Zhao, Haochen Liu, Hui Liu +6
Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically al…
Why Do You Spread This Message? Understanding Users Sentiment in Social Media Campaigns
Jalal Mahmud, Huiji Gao
Twitter has been increasingly used for spreading messages about campaigns. Such campaigns try to gain followers through their Twitter accounts, influence the followers and spread m…