Deep Character-Level Click-Through Rate Prediction for Sponsored Search

Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This is inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specially, the proposed architectures only consider the textual content appearing in a query-advertisement pair as input, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.Comment: SIGIR2017, 10 page

Matching Contextual Ads and Web Page Contents through Computational Advertising: Getting the Best Match

The technological transformation and automation of digital content delivery has revolutionized the media industry. What is more, the Internet is rapidly turning into an advertising channel. Just in the United States, Internet advertising revenues hit $7.3 billion for the first quarter of 2011, representing a 23 percent increase over the same period in 2010 (iab.net, 2011). Beneficiaries of this investment and growth are search engines such as Google, Yahoo, and MSN. Also, Malaysian advertising landscape is gradually shifting its traditional media forms to the emergent of Internet advertising but still at a budding stage. The latter shows much room for growth, as the industry fuels to content digitization on Web applications. In this project, the types of Internet advertising that is going to be discussed on are Contextual Ads and Sponsored Search Ads, but the major scope will be on Contextual Advertising. Given that, these types of advertising have the central challenge of finding the “best match” between a given context and a suitable advertisement, through principled way of computational methods. Hence, it is also referred as Computational advertising. Furthermore, there are four main players that exists in the Internet advertising ecosystem that are going to be discussed in this study, which are; Users, Advertisers, Ad Exchange and Publishers. Hence in order to find ways to counter the centre challenge, this research study will mainly address two objectives, which are to successfully make the best Contextual Ads selections that match to the Web Page contents through the concept of Computational advertising, and to ensure that there is a valuable connection between the Web pages and the Contextual Ads. Thus, the scope of the study will be mainly on discussing about the theory of Computational advertising itself, besides elaborating on Contextual Ads, matching Contextual Ads and Web pages and also, finding the most feasible way in creating the valuable connection between Contextual Ads and the Web pages. Moreover, at the end of every discussion in every subtopic, some insights on the Internet advertising in Malaysian context are discussed as per related issue. v Consequently, this study employed two main methods to address the research questions rose. Those methods include extensive research and analysis on previous literature works and journals, and also in depth surveys to collect related data and information in real-life situations. Every part of gathered data and findings will then be analyzed accordingly. All discussions, conclusion and future recommendations are presented as per sections. Hence in order to prove the working mechanism of matching Contextual Ads and Web pages by using Computational advertising approach, Web pages together with the ads matching system, will then be developed through FYP-II timeline, as the final product of the study

Semantic Search over Encrypted Data in Cloud Computing

Cloud storage becomes more and more popular in the recent trend since it provides various benefits over the traditional storage solutions. Along with many benefits provided by cloud storage, many security problems arise in cloud storage which prevents enterprises from migrate their data to cloud storage. These security problems induce the data owners to encrypt all their sensitive data such as social security number (SSN), credit card information, and personal tax information before they can be stored in cloud storage. The encryption approach may have strengthened the data security of cloud data, but it degrades the data efficiency because the encryption reduces the searchability of the data. Many schemes were proposed in recent researches which enable keyword search over encrypted data in cloud computing, and these schemes contain weaknesses which make them impractical when applying these schemes in real-life scenarios. In this project, we developed a system to support semantic search over encrypted data in cloud computing with three different schemes. The three schemes that we developed are “Synonym-Based Keyword Search (SBKS)”, “Wikipedia-Based Keyword Search (WBKS)”, and “Wikipedia-Based Synonym Keyword Search (WBSKS)”. Based on our experiment data, it demonstrated that the indexes created by our schemes are 95% smaller and reduced the average search time by 95% if compared to the schemes proposed previously. These improvements illustrated that our developed schemes are more practical than the former proposed schemes

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research