Multiple-Play Bandits in the Position-Based Model

Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the multiple-play semi-bandit setting. However, a major concern in this context is when the system cannot decide whether the user feedback for each item is actually exploitable. Indeed, much of the content may have been simply ignored by the user. The present work proposes to exploit available information regarding the display position bias under the so-called Position-based click model (PBM). We first discuss how this model differs from the Cascade model and its variants considered in several recent works on multiple-play bandits. We then provide a novel regret lower bound for this model as well as computationally efficient algorithms that display good empirical and theoretical performance

Crowdsourcing public transport data via livemobile tracking : Feasibility study of a system capable of collecting mobile data to build a database ofpublic transit routes, stops and timetables, using machine learning techniques andgraph theory

Millions of people use public transport systems everywhere in the world, while the number of smartphones connected to the Internet is dramatically increasing. The aim of this thesis is to study, design, and prototype a system to collect data from the mobile devices of regular public transport users and analyse them in order to provide useful information to travellers all over the world. To study the feasibility of this project at a large scale, data will be created to simulate what will be collected via mobile phone applications. The main objective of this paper is to study the effect of data mining techniques on mapping original transport routes with associated timetables.Crowdsourcing av kollektivtrafikdata via direkt mobilspårning. Miljontals personer använder kollektivtrafik överallt i världen och antalet smartphones med internetanslutning stiger dagligen. Avsikten med den här avhandlingen är att undersöka, skapa och testa ett system som samlar och analyserar data från mobiltelefoner av kollektivtrafikanvändare så att användbar information kan bli tillgänglig till resande världen över. För att generellt undersöka möjligheten med ett sådant projekt kommer data att skapas för att simulera informationen som är tänkt att senare samlas från resandes mobiletelefoner. Huvudsyftet med den här avhandlingen är att undersöka data mining effekter i kartläggningen av ursprungliga transportsträckor och dess tidtabeller

A Network-Aware Approach for Searching As-You-Type in Social Media

International audienceWe present in this paper a novel approach for as-you-type top-k keyword search over social media. We adopt a natural "network-aware" interpretation for information relevance, by which information produced by users who are closer to the seeker is considered more relevant. In practice, this query model poses new challenges for effectiveness and efficiency in online search, even when a complete query is given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and classic IR indexes such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing to output the most likely answer within any chosen running-time limit. We evaluate it through extensive experiments for several applications and search scenarios , including searching for posts in micro-blogging (Twitter and Tumblr), as well as searching for businesses based on reviews in Yelp. They show that our solution is effective in answering real-time as-you-type searches over social media

As-You-Type Social Aware Search

International audienceModern search applications feature real-time as-you-type query search. In its elementary form, the problem consists in retrieving a set of k search results, that is, performing a search with a given prefix, and showing the top-ranked results. In this article, we focus on as-you-type keyword search over social media, that is, data published by users who are interconnected through a social network. We adopt a " network-aware " interpretation for information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. This query model raises new challenges for effectiveness and efficiency in online search, even when the intent of the user is fully specified, as a complete query given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and traditional IR indexes, such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing output of the most likely answer within any chosen runtime limit. We evaluate our approach through extensive experiments for several applications and search scenarios. We consider searching for posts in microblogging (Twitter and Tumblr), for businesses (Yelp), as well as for movies (Amazon) based on reviews. We also conduct a series of experiments comparing our algorithm with baselines using state-of-the-art techniques and measuring the improvements brought by several key optimizations. They show that our solution is effective in answering real-time as-you-type searches over social media

Multiple-Play Bandits in the Position-Based Model

Abstract Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the multiple-play semi-bandit setting. However, a major concern in this context is when the system cannot decide whether the user feedback for each item is actually exploitable. Indeed, much of the content may have been simply ignored by the user. The present work proposes to exploit available information regarding the display position bias under the so-called Position-based click model (PBM). We first discuss how this model differs from the Cascade model and its variants considered in several recent works on multiple-play bandits. We then provide a novel regret lower bound for this model as well as computationally efficient algorithms that display good empirical and theoretical performance

Jeunes : de l'emploi aux modes de vie. Points de vue sur un champ de recherche

Paul Bouffartigue, Jean-Charles Lagree, José Rose, Die Jugendlichen : von der Beschäftigung hin zu der Lebensart. Beurteilung eines Forschungsbereiches. Auf der Basis einer Bilanz über die französischen Forschungsarbeiten der zehn letzten Jahre im Feld der Arbeit und der Lebensart der Jugendlichen und unter Berücksichtigung auf dessen Fähigkeit, die Zusammenhänge zwischen beiden Bereichen zu behandeln, versuchen die Verfasser, das Versprechen neuer Forschungsansätze zu erfassen. Unter der Wirkung einer bedeutenden institutionellen Organisation und der starken Sozialnachfrage, die aus der Krise und der Wandlung der Arbeit entstanden ist und die den Nachwuchs besonders trifft, ist der Fortschritt der statistischen Kenntnisse über die Beziehungen zwischen Bildung und Beschäftigung und über die berufliche Eingliederung der Jugendlichen von einer wesentlichen Konvergenz der Forschungsarbeiten — theoretische Ansätze wie auch empirische Ergebnisse — begleitet worden. Das Schema der Anpassung zwischen Bildung und Beschäftligung is durch das eines dynamischen Prozesses von steigender Dauer ersetzt worden, innerhalb dessen Selektionsprozesse stattfinden, die zu der Auflösung der Kategorie « Jugendliche » beitragen. Solche Begriffe wie der « Berufsverlauf » oder die « Übergangsphase » haben einen immer grösseren Erfolg. Trotz der schwachen theoretischen Verankerung der meisten Erhebungen kann der Einfluss der Paradigma « Reproduktion » und « Segmentation » leicht erkannt werden. Die Entwicklung der Längsschnitt-Ansätze zur Eingliederung in das Erwachsenenleben scheint eine der aufschlussreichsten Zielrichtungen zu sein, um die Grenzen der Paradigma zu übersteigen und um den sozialen Praktiken, durch welche die « Jugendlichen » zu den forstchreitenden gesellschaftlichen Veränderungen beitragen, ihre ganze Bedeutung wieder zu geben.Paul Bouffartigue, Jean-Charles Lagree, José Rose, Young people : from employment to lifestyle. Viewpoint of a research area. Using the results of research performed during the last ten years in the field of young peoples' employment and lifestyle, especially from the point of view of their ability to deal with relationships between these two areas, an attempt has been made to grasp the promises contained in new directions of research. Under the influence of powerful institutional organization, and the strength of social demand springing from the crisis and changes in employment, affecting in particular the younger generation in our country, the progress in statistical knowledge of reports on employment/training and professional integration of young people is accompanied by a notable convergence of research, both in terms of approach and empirical results. The adequation schema between employment and training has been replaced by a dynamic process of increasing duration during which a sorting operation is carried out, contributing to the splintering of the category of young people. Concepts such as that of « career patterns » or « professional transition » are meeting with increasing favour. Despite the slight explicit theoretical basis of most of the surveys, the influence of the paradigms, of « reproduction » and « segmentation » can easily be spotted. The development of longitudinal approaches to the beginning of adult life appears to be one of the most promising directions for going beyond these paradigms and restoring their proper weight to the social practices through which « the young » contribute to current social changes.À partir d'un bilan des recherches françaises des dix dernières années dans le champ de l'emploi et des modes de vie des jeunes, du point de vue notamment de leur capacité à traiter des rapports entre ces deux domaines, on s'efforce de saisir les promesses dont sont porteuses de nouvelles orientations de recherche. Sous l'effet d'une organisation institutionnelle puissante et de la force de la demande sociale née de la crise et des mutations de l'emploi, touchant particulièrement les nouvelles générations dans notre pays, le progrès des connaissances statistiques des rapports emploi-formation et de l'insertion professionnelle des jeunes s'est accompagné d'une convergence notable des travaux, en termes d'approches comme de résultats empiriques. Au schéma d'adéquation emploi-formation s'est substitué celui d'un processus dynamique de durée croissante au travers duquel s'opèrent des tris qui contribuent à l'éclatement de la catégorie « jeunesse ». Des notions comme celles de « cheminement professionnel » ou de « transition professionnelle » connaissent un succès croissant. Malgré le faible ancrage théorique explicite de la plupart des enquêtes, l'influence des paradigmes de la reproduction, de la segmentation, peut être aisément repérée. Le développement d'approches longitudinales de l'entrée dans la vie adulte apparaît être un des axes les plus prometteurs pour dépasser les limites de ces paradigmes et restituer toute leur épaisseur aux pratiques sociales au travers desquelles les jeunes contribuent aux transformations sociales en cours.Bouffartigue Paul, Lagree Jean-Charles, Rose José. Jeunes : de l'emploi aux modes de vie. Points de vue sur un champ de recherche . In: Formation Emploi. N.26, 1989. pp. 63-78

Algorithms for Online Influencer Marketing

International audienceInfluence maximization is the problem of finding influential users, or nodes, in a graph so as to maximize the spread of information. It has many applications in advertising and marketing on social networks. In this paper, we study a highly generic version of influence maximization, one of optimizing influence campaigns by sequentially selecting “spread seeds” from a set of influencers, a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain persistently active. This problem is in particular relevant for an important form of online marketing, known as influencer marketing, in which the marketers target a sub-population of influential people, instead of the entire base of potential buyers. Importantly, we make no assumptions on the underlying diffusion model and we work in a setting where neither a diffusion network nor historical activation data are available. We call this problem online influencer marketing with persistence (in short, OIMP). We first discuss motivating scenarios and present our general approach. We introduce an estimator on the influencers’ remaining potential – the expected number of nodes that can still be reached from a given influencer – and justify its strength to rapidly estimate the desired value, relying on real data gathered from Twitter. We then describe a novel algorithm, GT-UCB, relying on probabilistic upper confidence bounds on the remaining potential. We show that our approach leads to high-quality spreads on both simulated and real datasets. Importantly, it is orders of magnitude faster than state-of-the-art influence maximization methods, making it possible to deal with large-scale online scenarios