Author
Ning Liu
Other affiliations: Data Storage Institute, National University of Singapore
Bio: Ning Liu is an academic researcher from Microsoft. The author has contributed to research in topics: Web query classification & Web search query. The author has an hindex of 19, co-authored 67 publications receiving 1415 citations. Previous affiliations of Ning Liu include Data Storage Institute & National University of Singapore.
Papers
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20 Apr 2009TL;DR: This work is the first empirical study for BT on the click-through log of real world ads and draws three important conclusions: users who clicked the same ad will truly have similar behaviors on the Web, Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search.
Abstract: Behavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking at how much BT can truly help online advertising in commercial search engines. To answer this question, in this paper we provide an empirical study on the click-through log of advertisements collected from a commercial search engine. From the comprehensively experiment results on the sponsored search log of the commercial search engine over a period of seven days, we can draw three important conclusions: (1) Users who clicked the same ad will truly have similar behaviors on the Web; (2) Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search; (3) Using the short term user behaviors to represent users is more effective than using the long term user behaviors for BT. The statistical t-test verifies that all conclusions drawn in the paper are statistically significant. To the best of our knowledge, this work is the first empirical study for BT on the click-through log of real world ads.
386 citations
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TL;DR: A novel immersed boundary velocity correction-lattice Boltzmann method is presented and validated in this work by its application to simulate the two-dimensional flow over a circular cylinder, which directly corrects the velocity to enforce the physical boundary condition.
186 citations
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16 Sep 2011TL;DR: In this article, semantic user profiles are built through extraction of online user behavior data such as user search queries and page views, and include user interest information that is inferred based on user behavior.
Abstract: Embodiments facilitate greater flexibility in definition of user segments for targeted advertising, by employing indexed semantic user profiles. Semantic user profiles are built through extraction of online user behavior data such as user search queries and page views, and include user interest information that is inferred based on user behavior. Semantic user profiles are then indexed to facilitate search for a set of users that fit specified semantic search terms. Search results for semantic profiles are ranked according to a ranking model developed through machine learning. In some embodiments, building and indexing of semantic profiles and learning of the ranking model is performed offline to facilitate more efficient online processing of queries.
60 citations
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28 Jun 2007TL;DR: In this article, a query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent, based on causal relationships with other queries.
Abstract: Techniques for analyzing and modeling the frequency of queries are provided by a query analysis system. A query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent. The query analysis system forecasts the frequency of time-dependent queries based on their periodicities. The query analysis system forecasts the frequency of time-independent queries based on causal relationships with other queries. To forecast the frequency of time-independent queries, the query analysis system analyzes the frequency of a query over time to identify significant increases in the frequency, which are referred to as “query events” or “events.” The query analysis system forecasts frequencies of time-independent queries based on queries with events that tend to causally precede events of the query to be forecasted.
58 citations
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28 Jun 2009TL;DR: This work proposes a novel user segmentation algorithm named Probabilistic Latent Semantic User Segmentation (PLSUS), which adopts the probabilistic latent semantic analysis to mine the relationship between users and their behaviors so as to segment users in a semantic manner.
Abstract: Behavioral Targeting (BT), which aims to deliver the most appropriate advertisements to the most appropriate users, is attracting much attention in online advertising market. A key challenge of BT is how to automatically segment users for ads delivery, and good user segmentation may significantly improve the ad click-through rate (CTR). Different from classical user segmentation strategies, which rarely take the semantics of user behaviors into consideration, we propose in this paper a novel user segmentation algorithm named Probabilistic Latent Semantic User Segmentation (PLSUS). PLSUS adopts the probabilistic latent semantic analysis to mine the relationship between users and their behaviors so as to segment users in a semantic manner. We perform experiments on the real world ad click through log of a commercial search engine. Comparing with the other two classical clustering algorithms, K-Means and CLUTO, PLSUS can further improve the ads CTR up to 100%. To our best knowledge, this work is an early semantic user segmentation study for BT in academia.
45 citations
Cited by
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TL;DR: This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies currently adopt to deal with the Big Data problems.
2,516 citations
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TL;DR: A comprehensive introduction to a large body of research, more than 200 key references, is provided, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix.
Abstract: Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.
777 citations
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TL;DR: In this paper, the authors investigated the relationship between environmental degradation and economic growth and found that financial liberalization may be harmful for environmental quality if it is not accomplished in a strong institutional framework.
766 citations
01 Nov 2002
TL;DR: An efficient ghost-cell immersed boundary method (GCIBM) for simulating turbulent flows in complex geometries is presented in this paper, where a boundary condition is enforced through a ghost cell method.
Abstract: An efficient ghost-cell immersed boundary method (GCIBM) for simulating turbulent flows in complex geometries is presented. A boundary condition is enforced through a ghost cell method. The reconstruction procedure allows systematic development of numerical schemes for treating the immersed boundary while preserving the overall second-order accuracy of the base solver. Both Dirichlet and Neumann boundary conditions can be treated. The current ghost cell treatment is both suitable for staggered and non-staggered Cartesian grids. The accuracy of the current method is validated using flow past a circular cylinder and large eddy simulation of turbulent flow over a wavy surface. Numerical results are compared with experimental data and boundary-fitted grid results. The method is further extended to an existing ocean model (MITGCM) to simulate geophysical flow over a three-dimensional bump. The method is easily implemented as evidenced by our use of several existing codes.
740 citations