Search advertising for small Finnish tourism company in Russian Internet – Experimental study: Google AdWords versus Yandex.Direct

Yandex is the dominant search engine in Russia, followed by the world leader Google. This study focuses on the performance differences between the two in search advertising in the context of tourism, by running two identical campaigns and measuring the KPI’s, such as CPA (cost-per-action), on both campaigns. Search engine advertising is a new and fast changing form of advertising, which should be studied frequently in order to keep up with the changes. Research was done as an experimental study in cooperation with a Finnish tourism company and the data is gathered from the clickstream and not from questionnaires, which is recommended method by the literature. The results of the study suggests that Yandex.Direct performed better in the selected niche and that the individual campaign planning for Yandex.Direct and Google AdWords is an important part of the optimization of search advertising in Russia.

Using interaction data for improving the offline and online evaluation of search engines

This thesis investigates how web search evaluation can be improved using historical interaction data. Modern search engines combine offline and online evaluation approaches in a sequence of steps that a tested change needs to pass through to be accepted as an improvement and subsequently deployed. We refer to such a sequence of steps as an evaluation pipeline. In this thesis, we consider the evaluation pipeline to contain three sequential steps: an offline evaluation step, an online evaluation scheduling step, and an online evaluation step. In this thesis we show that historical user interaction data can aid in improving the accuracy or efficiency of each of the steps of the web search evaluation pipeline. As a result of these improvements, the overall efficiency of the entire evaluation pipeline is increased. Firstly, we investigate how user interaction data can be used to build accurate offline evaluation methods for query auto-completion mechanisms. We propose a family of offline evaluation metrics for query auto-completion that represents the effort the user has to spend in order to submit their query. The parameters of our proposed metrics are trained against a set of user interactions recorded in the search engine’s query logs. From our experimental study, we observe that our proposed metrics are significantly more correlated with an online user satisfaction indicator than the metrics proposed in the existing literature. Hence, fewer changes will pass the offline evaluation step to be rejected after the online evaluation step. As a result, this would allow us to achieve a higher efficiency of the entire evaluation pipeline. Secondly, we state the problem of the optimised scheduling of online experiments. We tackle this problem by considering a greedy scheduler that prioritises the evaluation queue according to the predicted likelihood of success of a particular experiment. This predictor is trained on a set of online experiments, and uses a diverse set of features to represent an online experiment. Our study demonstrates that a higher number of successful experiments per unit of time can be achieved by deploying such a scheduler on the second step of the evaluation pipeline. Consequently, we argue that the efficiency of the evaluation pipeline can be increased. Next, to improve the efficiency of the online evaluation step, we propose the Generalised Team Draft interleaving framework. Generalised Team Draft considers both the interleaving policy (how often a particular combination of results is shown) and click scoring (how important each click is) as parameters in a data-driven optimisation of the interleaving sensitivity. Further, Generalised Team Draft is applicable beyond domains with a list-based representation of results, i.e. in domains with a grid-based representation, such as image search. Our study using datasets of interleaving experiments performed both in document and image search domains demonstrates that Generalised Team Draft achieves the highest sensitivity. A higher sensitivity indicates that the interleaving experiments can be deployed for a shorter period of time or use a smaller sample of users. Importantly, Generalised Team Draft optimises the interleaving parameters w.r.t. historical interaction data recorded in the interleaving experiments. Finally, we propose to apply the sequential testing methods to reduce the mean deployment time for the interleaving experiments. We adapt two sequential tests for the interleaving experimentation. We demonstrate that one can achieve a significant decrease in experiment duration by using such sequential testing methods. The highest efficiency is achieved by the sequential tests that adjust their stopping thresholds using historical interaction data recorded in diagnostic experiments. Our further experimental study demonstrates that cumulative gains in the online experimentation efficiency can be achieved by combining the interleaving sensitivity optimisation approaches, including Generalised Team Draft, and the sequential testing approaches. Overall, the central contributions of this thesis are the proposed approaches to improve the accuracy or efficiency of the steps of the evaluation pipeline: the offline evaluation frameworks for the query auto-completion, an approach for the optimised scheduling of online experiments, a general framework for the efficient online interleaving evaluation, and a sequential testing approach for the online search evaluation. The experiments in this thesis are based on massive real-life datasets obtained from Yandex, a leading commercial search engine. These experiments demonstrate the potential of the proposed approaches to improve the efficiency of the evaluation pipeline.

Traduzione automatica neurale in ambito economico-finanziario: analisi e valutazione della qualità di sette sistemi

A partire dagli anni ‘40, l'uso della traduzione automatica ha iniziato a rappresentare un soggetto fondamentale nella traduzione, costituendo ancora ad oggi il centro di numerose ricerche e studi. In seguito alla diffusione della tecnologia informatica e del World Wide Web, iniziarono a essere disponibili sistemi commerciali di traduzione automatica. In questo periodo, divennero comuni diversi software di traduzione automatica gratuiti, fino a quando, alla fine degli anni 2000, si assistette infine allo sviluppo di una nuova tecnologia di apprendimento automatico neurale, basata sul funzionamento della mente umana. Il presente lavoro si concentra sulla valutazione della qualità della traduzione automatica neurale attraverso l'analisi e il confronto di sette sistemi, ovvero Google, Systran, Prompt, Microsoft, Yandex, ModernMT e Deepl, impiegati nel settore finanziario per la coppia linguistica inglese-italiano. Il primo capitolo tratta la storia della traduzione automatica, fornendo una breve descrizione delle diverse architetture, dei loro ambiti di utilizzo e della valutazione dei sistemi. Il secondo capitolo introduce il concetto di post-editing insieme agli obiettivi e le problematiche che lo caratterizzano. Il terzo capitolo presenta il progetto Intento, seguito da una breve panoramica dei sistemi di traduzione automatica analizzati. Nel quarto capitolo viene delineato il dominio finanziario, concentrandosi sui diversi ambiti che si sono intersecati con esso nell’ambito dello studio svolto. Il quinto e ultimo capitolo riguarda l'analisi dei segmenti tradotti, preceduta dalla definizione dei parametri scelti per la valutazione. L'ultima parte del capitolo illustra una sintesi dei risultati ottenuti e alcune considerazioni finali.