Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance

Managing economic and Islamic research in big data environment: from computer science perspective / Nordin Abu Bakar

Research in economic and Islamic fields are facing a major challenge in the surge of big data. The landscape and the environment produce problems of massive magnitude and demand robust solutions. The traditional method might not be able to cater for this huge challenge; so, researchers must embark on the mission to seek new and versatile methods to solve the complex problem. If not, the research output would end up with sub-optimal results. In computer science, there are machine learning algorithms that have been used to solve problems in a such complex environment. This article explains the current demanding situation facing many researchers and how those algorithms have successfully solved some of the problems. The potential applications of the methods should be learned and utilised to improve the outcome of the research in these field

Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page

Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance

Performance Modeling and Resource Management for Mapreduce Applications

Big Data analytics is increasingly performed using the MapReduce paradigm and its open-source implementation Hadoop as a platform choice. Many applications associated with live business intelligence are written as complex data analysis programs defined by directed acyclic graphs of MapReduce jobs. An increasing number of these applications have additional requirements for completion time guarantees. The advent of cloud computing brings a competitive alternative solution for data analytic problems while it also introduces new challenges in provisioning clusters that provide best cost-performance trade-offs. In this dissertation, we aim to develop a performance evaluation framework that enables automatic resource management for MapReduce applications in achieving different optimization goals. It consists of the following components: (1) a performance modeling framework that estimates the completion time of a given MapReduce application when executed on a Hadoop cluster according to its input data sets, the job settings and the amount of allocated resources for processing it; (2) a resource allocation strategy for deadline-driven MapReduce applications that automatically tailors and controls the resource allocation on a shared Hadoop cluster to different applications to achieve their (soft) deadlines; (3) a simulator-based solution to the resource provision problem in public cloud environment that guides the users to determine the types and amount of resources that should lease from the service provider for achieving different goals; (4) an optimization strategy to automatically determine the optimal job settings within a MapReduce application for efficient execution and resource usage. We validate the accuracy, efficiency, and performance benefits of the proposed framework using a set of realistic MapReduce applications on both private cluster and public cloud environment

Paid Search Advertising como meio de desenvolvimento de Brand Awareness para a conversão de Leads

A inovação tecnológica trouxe mudanças teóricas na maneira como o marketing é aplicado, passando a haver um maior interesse das empresas pela prática de paid search advertising, devido à maior eficácia na entrega de uma mensagem. Simultaneamente, a concorrência cada vez mais acirrada em muitos dos sectores de serviços B2B, tem levado as organizações a procurar uma vantagem competitiva por meio da notoriedade da marca. Diante desses desafios, a SGS Academy enquanto departamento de formação profissional do grupo SGS, passou a procurar novas formas de comunicar digitalmente, de modo a promover uma maior aproximação do negócio ao seu mercado e converter um maior número de leads através dos seus canais digitais. Para tal, delineou-se uma estratégia assente na criação e monitorização de uma campanha promocional com recurso a anúncios pay-per-click na plataforma Google Ads. O presente documento académico apoia-se numa técnica de análise de conteúdo e comprova que a introdução de uma campanha comunicacional de paid search advertising, assente na criação e monitorização de anúncios PPC, atuou como um meio eficaz para o aumento da brand awareness da SGS Academy e consequente conversão de leads no ecossistema digital da marca.The technological innovation has brought theoretical changes in the way marketing is applied, and companies are now more interested in the practice of paid search advertising, due to the greater effectiveness in delivering a message. Simultaneously, the increasingly fierce competition in many of the B2B service sectors has led organizations to seek competitive advantage through brand awareness. Facing these challenges, SGS Academy, as a professional training department of the SGS group, started looking for new ways to communicate digitally, in order to promote a closer approach of the business to its market and convert a greater number of leads through their digital channels. To this end, it was outlined a strategy based on the creation and monitoring of a promotional campaign using pay-per-click ads on the Google Ads platform. This academic document is supported by a content analysis technique and proves that the introduction of a paid search advertising campaign, based on the creation and monitoring of PPC ads, acted as an effective means to increase SGS Academy brand awareness and consequent conversion of leads in the digital ecosystem of the brand