Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

Sponsored search represents a major source of revenue for web search engines. This popular advertising model brings a unique possibility for advertisers to target users' immediate intent communicated through a search query, usually by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries it is challenging for advertisers to identify all such relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advanced matching approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage, and incremental revenue. Lastly, we open-source learned query embeddings to be used by researchers in computational advertising and related fields.Comment: 10 pages, 4 figures, 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Ital

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

Interactive Food and Beverage Marketing: Targeting Children and Youth in the Digital Age

Looks at the practices of food and beverage industry marketers in reaching youth via digital videos, cell phones, interactive games and social networking sites. Recommends imposing governmental regulations on marketing to children and adolescents

Distributed Information Retrieval using Keyword Auctions

This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions

Suljettujen online-mainosalustojen strategiat — tapaukset Google ja Facebook

This thesis studies closed ad platforms in the modern online advertising industry. The research in the field is still nascent and the concept of a closed ad platform doesn’t exist. The objective of the research was to discover the main factors determining the revenue of online advertising platforms and to understand why some publishers choose to establish their own closed ad platforms instead of selling their inventory for third-party ad platforms. The concept of a closed ad platform is defined leveraging the existing online advertising literature and the platform governance structure theory. Using the case study method, Google and Facebook were chosen as the cases as they have driven most of the innovation in the field and quickly gained significant market share. In total, 47 people were interviewed for this study, most of them working for advanced online advertisers. Based on the interviews, a microeconomic mathematic formula is created for modeling an ad platform’s net advertising revenue. The formula is used to identify the five main drivers of an ad platform’s revenue an each of them are studied in depth. The results suggest that the most important revenue drivers the ad platforms can affect are access to an active user base, the efficiency of ad serving and the comprehensiveness of measurement. Setting up a closed ad platform requires significant investments from a publisher and should be only done if it can improve the advertisers’ results. After it’s been established, a closed platform can leverage its position to collect user data and structured business data to optimize its performance further. The results provide a structured understanding of the main dynamics in the industry that can be used in decision-making and a basis for future research on closed ad platforms.Tämä diplomityö tutkii suljettuja mainosalustoja nykyaikaisella online-mainonta-alalla. Alan tutkimus on vielä aluillaan ja suljetun mainosalustan konseptia ei ole olemassa. Tämän tutkimuksen tavoitteena oli löytää online-mainosalustojen liikevaihdon määrittävät tekijät ja ymmärtää miksi jotkut julkaisijat valitsevat omien suljettujen mainosalustojen perustamisen mainospaikkojen kolmansien osapuolien mainosalustoille myymisen sijaan. Suljetun mainosalustan konsepti määritellään olemassaolevaa online- mainontakirjallisuutta ja alustojen hallintarakenneteoriaa hyödyntäen. Tapaustutkimusmenetelmää käyttäen, Google ja Facebook valittiin tapauksiksi, sillä ne ovat ajaneet eniten innovaatioita alalla ja nopeasti saavuttaneet merkittävän markkinaosuuden. Yhteensä 47 henkilöä haastateltiin tätä tutkimusta varten, useimmat heistä edistyneiden online-mainostajien työntekijöitä. Haastattelujen perusteella luodaan mikrotaloudellinen matemaattinen kaava mainosalustan nettoliikevaihdon mallintamiseksi. Kaavaa käytetään tunnistamaan mainosalustan liikevaihdon viisi pääkomponenttia, ja kuhunkin niistä perehdytään syvällisemmin. Tulokset viittaavat, että tärkeimmät liikevaihdon ajurit, joihin mainosalustat voivat vaikuttaa ovat pääsy aktiiviseen käyttäjäkantaan, mainosten näyttämisen tehokkuus ja mittaamisen kattavuus. Suljetun mainosalustan perustaminen vaatii merkittäviä investointeja julkaisijalta ja tulisi tehdä ainoastaan, jos sillä voidaan parantaa mainostajien tuloksia. Suljetun alustan perustamisen jälkeen sen positiota voidaan hyödyntää käyttäjädatan ja strukturoidun liiketoimintadatan keräämiseksi suorituskyvyn edelleen optimoimiseksi. Tulokset tarjoavat toimialan päädynamiikkojen ymmärryksen, jota voidaan käyttää päätöksenteossa sekä pohjana suljettujen mainosalustojen edelleen tutkimiseksi tulevaisuudessa

Multichannel in a complex world

The proliferation of devices and channels has brought new challenges to just about every organisation in delivering consistently good customer experiences and effectively joining up service provision with marketing activity, data and content. A good multichannel strategy and execution is increasingly becoming essential to marketers and customer experience professionals from every sector. This report seeks to identify the key issues, challenges and opportunities that surround multichannel and provide some best practice insight and principles on the elements that are key to multichannel success. As part of the research for this report, we spoke to six experienced customer experience and marketing practitioners from large organisations across different sectors. In Multichannel Marketing: Metrics and Methods for On and Offline Success, Akin Arikan (2008) said: ‘Because customers are multichannel beings and demand relevant, consistent experiences across all channels, businesses need to adopt a multichannel mind-set when listening to their customers.’ It was clear from the companies interviewed for this report that it remains challenging for many organisations to maintain consistency across so many customer touchpoints. Not only that, but the ability to balance consistency with the capability to fully exploit the unique attributes of each channel remains an aspiration for many. The proliferation of devices and digital channels has added complexity to customer journeys, making issues around the joining up of customer experience and the attribution of value of key importance to many. Whilst senior leaders within the organisations spoken to seem to be bought in to multichannel, this buy-in was not always replicated across the rest of the organisation and did not always translate into a cohesive multichannel strategy. A number of companies were undertaking work around customer journey mapping and customer segmentation, using a variety of passive and actively collected data in order to identify specific areas of poor customer experience and create action plans for improvement. Others were undertaking projects using sophisticated tracking and tagging technologies to develop an understanding of the value and role of specific channels and to provide better intelligence to the business on attribution that might be used to inform future investment decisions. A consistent barrier to improving customer experience is the ability to join up many different legacy systems and data in order to provide a single customer view and form the basis for delivery of a more consistent and cohesive multichannel approach. Whilst there remain significant challenges around multichannel, there are some useful technologies allowing businesses to develop better insight into customer motivation and activity. Nonetheless, delivery of seamless multichannel experience remains a work-inprogress for many