| ChengLang Lu|
(Alternative names for this author)
|Co-authors||Chen E., GuanDong Xu, Haisu Zhang, Lam W., Peter Dolog, YanChun Zhang, ZongDa Wu|
|Authorship||Publications (3), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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ChengLang Lu is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Position-wise contextual advertising: Placing relevant ads at appropriate positions of a web page||Contextual advertising
|Neurocomputing||English||2013||Web advertising, a form of online advertising, which uses the Internet as a medium to post product or service information and attract customers, has become one of the most important marketing channels. As one prevalent type of web advertising, contextual advertising refers to the placement of the most relevant ads at appropriate positions of a web page, so as to provide a better user experience and increase the user's ad-click rate. However, most existing contextual advertising techniques only take into account how to select as relevant ads for a given page as possible, without considering the positional effect of the ad placement on the page, resulting in an unsatisfactory performance in ad local context relevance. In this paper, we address the novel problem of position-wise contextual advertising, i.e., how to select and place relevant ads properly for a target web page. In our proposed approach, the relevant ads are selected based on not only global context relevance but also local context relevance, so that the embedded ads yield contextual relevance to both the whole target page and the insertion positions where the ads are placed. In addition, to improve the accuracy of global and local context relevance measure, the rich wikipedia knowledge is used to enhance the semantic feature representation of pages and ad candidates. Last, we evaluate our approach using a set of ads and pages downloaded from the Internet, and demonstrate the effectiveness of our approach. © 2013 Elsevier B.V.||0||0|
|An Improved Contextual Advertising Matching Approach based on Wikipedia Knowledge||Comput. J.||English||2012||0||0|
|Twitter user modeling and tweets recommendation based on wikipedia concept graph||AAAI Workshop - Technical Report||English||2012||As a microblogging service, Twitter is playing a more and more important role in our life. Users follow various accounts, such as friends or celebrities, to get the most recent information. However, as one follows more and more people, he/she may be overwhelmed by the huge amount of status updates. Twitter messages are only displayed by time recency, which means if one cannot read all messages, he/she may miss some important or interesting tweets. In this paper, we propose to re-rank tweets in user's timeline, by constructing a user profile based on user's previous tweets and measuring the relevance between a tweet and user interest. The user interest profile is represented as concepts from Wikipedia, which is quite a large and inter-linked online knowledge base. We make use of Explicit Semantic Analysis algorithm to extract related concepts from tweets, and then expand user's profile by random walk on Wikipedia concept graph, utilizing the inter-links between Wikipedia articles. Our experiments show that our model is effective and efficient to recommend tweets to users. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.||0||0|