Items by Yan, Jun

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 how much BT can truly help online advertising in search engines. In this paper we provide an empirical study on the click-through log of advertisements collected from a commercial search engine. From the experiment results over a period of seven days, we 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 short term user behaviors to represent users is more effective than using long term user behaviors for BT. We conducted statistical t-test which verified 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.

A pressing task during the unification process is to identify a user’s vertical search intention based on the user’s query. In this paper, we propose a novel method to propagate social annotation, which includes user-supplied tag data, to both queries and VSEs for semantically bridging them. Our proposed algorithm consists of three key steps: query annotation, vertical annotation and query intention identification. Our algorithm, referred to as TagQV, verifies that the social tagging can be propagated to represent Web objects such as queries and VSEs besides Web pages. Experiments on real Web search queries demonstrate the effectiveness of TagQV in query intention identification.

In this paper, we propose to model the blended search problem by assuming conditional dependencies among queries, VSEs and search results. The probability distributions of this model are learned from search engine query log through unigram language model. Our experimental exploration shows that, (1) a large number of queries in generic Web search have vertical search intentions; and (2) our proposed algorithm can effectively blend vertical search results into generic Web search, which can improve the Mean Average Precision (MAP) by as much as 16% compared to traditional Web search without blending. these components into a single list. However, from the classical meta-search problem’s configuration, the query log of component search engines is not available for study. In this extended abstract, we model the blended search problem based on the conditional dependencies among queries, VSEs and all the search results. We utilize the usage information, i.e. query log, of all the VSEs, which are not available for traditional metasearch engines, to learn the model parameters by the smoothed unigram language model. Finally, given a user query, the search results from both generic Web search and different VSEs are ranked together by inferring their probabilities of relevance to the given query. The main contributions of this work are, (1) through studying the belonging vertical search engines’ query log of a commercial search engine, we show the importance of blended search problem; (2) we propose a novel probabilistic model based approach to explore the blended search problem; and (3) we experimentally verify that our proposed algorithm can effectively blend vertical search results into generic Web search, which can improve the MAP as much as 16% in contrast to traditional Web search without vertical search blending and 10% to some other some ranking baseline.

Both search engine click-through log and social annotation have been utilized as user feedback for search result re-ranking. However, to our best knowledge, no previous study has explored the correlation between these two factors for the task of search result re-ranking. In this paper, we show that the gap between search queries and social tags of the same web page can well reflect its user preference score. Motivated by this observation, we propose a novel algorithm, called Query-Tag-Gap (QTG), to rerank search results for better user satisfaction. Intuitively, on one hand, the search users’ intentions are generally described by their queries before they read the search results. On the other hand, the web annotators semantically tag web pages after they read the content of the pages. The difference between users’ recognition of the same page before and after they read it is a good reflection of user satisfaction. In this extended abstract, we formally define the query set and tag set of the same page as users’ pre- and postknowledge respectively. We empirically show the strong correlation between user satisfaction and user’s knowledge gap before and after reading the page. Based on this gap, experiments have shown outstanding performance of our proposed QTG algorithm in search result re-ranking.