6 citations · 8 across the 6 of their papers we have counts for
7 papers
Automatic Historical Feature Generation through Tree-based Method in Ads Prediction
Hongjian Wang, Qi Li, Lanbo Zhang +4
Historical features are important in ads click-through rate (CTR) prediction, because they account for past engagements between users and ads. In this paper, we study how to effici…
Managing Diversity in Airbnb Search
Mustafa Abdool, Malay Haldar, Prashant Ramanathan +5
One of the long-standing questions in search systems is the role of diversity in results. From a product perspective, showing diverse results provides the user with more choice and…
Improving Deep Learning For Airbnb Search
Malay Haldar, Mustafa Abdool, Prashant Ramanathan +6
The application of deep learning to search ranking was one of the most impactful product improvements at Airbnb. But what comes next after you launch a deep learning model? In this…
Identifying Relevant Document Facets for Keyword-Based Search Queries
Lanbo Zhang
As structured documents with rich metadata (such as products, movies, etc.) become increasingly prevalent, searching those documents has become an important IR problem. Although ad…
Interactive Retrieval Based on Wikipedia Concepts
Lanbo Zhang
This paper presents a new user feedback mechanism based on Wikipedia concepts for interactive retrieval. In this mechanism, the system presents to the user a group of Wikipedia con…
Learning from Labeled Features for Document Filtering
Lanbo Zhang, Yi Zhang, Qianli Xing
Existing document filtering systems learn user profiles based on user relevance feedback on documents. In some cases, users may have prior knowledge about what features are importa…
Hierarchical Bayesian Models with Factorization for Content-Based Recommendation
Lanbo Zhang, Yi Zhang
Most existing content-based filtering approaches learn user profiles independently without capturing the similarity among users. Bayesian hierarchical models \cite{Zhang:Efficient}…