48 citations · 72 across the 13 of their papers we have counts for
13 papers
JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling
Daochen Zha, Chun How Tan, Xin Liu +9
Sequence modeling has become increasingly popular in recommendation and ranking algorithms, owing to its capacity to model users' historical behaviors and infer user intentions. De…
Recommending Search Filters To Improve Conversions At Airbnb
Hao Li, Kedar Bellare, Siyu Yang +4
Airbnb, a two-sided online marketplace connecting guests and hosts, offers a diverse and unique inventory of accommodations, experiences, and services. Search filters play an impor…
Debunking Disinformation: Revolutionizing Truth with NLP in Fake News Detection
Li He, Siyi Hu, Ailun Pei
The Internet and social media have altered how individuals access news in the age of instantaneous information distribution. While this development has increased access to informat…
STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Yachen Kang, Li He, Jinxin Liu +2
Preference-based reinforcement learning (PbRL) promises to learn a complex reward function with binary human preference. However, such human-in-the-loop formulation requires consid…
Beyond Reward: Offline Preference-guided Policy Optimization
Yachen Kang, Diyuan Shi, Jinxin Liu +2
This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online…
CEIL: Generalized Contextual Imitation Learning
Jinxin Liu, Li He, Yachen Kang +3
In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired…
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
Jinxin Liu, Lipeng Zu, Li He +1
Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. Howev…
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…
OER: Offline Experience Replay for Continual Offline Reinforcement Learning
Sibo Gai, Donglin Wang, Li He
The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline t…
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…
Click-Through Rate Prediction with Multi-Modal Hypergraphs
Li He, Hongxu Chen, Dingxian Wang +3
Advertising is critical to many online e-commerce platforms such as e-Bay and Amazon. One of the important signals that these platforms rely upon is the click-through rate (CTR) pr…
Off-policy Learning for Multiple Loggers
Li He, Long Xia, Wei Zeng +3
It is well known that the historical logs are used for evaluating and learning policies in interactive systems, e.g. recommendation, search, and online advertising. Since direct on…
Differential Equations for Modeling Asynchronous Algorithms
Li He, Qi Meng, Wei Chen +2
Asynchronous stochastic gradient descent (ASGD) is a popular parallel optimization algorithm in machine learning. Most theoretical analysis on ASGD take a discrete view and prove u…