Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation

Venue recommendation aims to assist users by making personalised suggestions of venues to visit, building upon data available from location-based social networks (LBSNs) such as Foursquare. A particular challenge for this task is context-aware venue recommendation (CAVR), which additionally takes the surrounding context of the user (e.g. the user’s location and the time of day) into account in order to provide more relevant venue suggestions. To address the challenges of CAVR, we describe two approaches that exploit word embedding techniques to infer the vector-space representations of venues, users’ existing preferences, and users’ contextual preferences. Our evaluation upon the test collection of the TREC 2015 Contextual Suggestion track demonstrates that we can significantly enhance the effectiveness of a state-of-the-art venue recommendation approach, as well as produce context-aware recommendations that are at least as effective as the top TREC 2015 systems

Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation

Venue recommendation aims to assist users by making personalised suggestions of venues to visit, building upon data available from location-based social networks (LBSNs) such as Foursquare. A particular challenge for this task is context-aware venue recommendation (CAVR), which additionally takes the surrounding context of the user (e.g. the user’s location and the time of day) into account in order to provide more relevant venue suggestions. To address the challenges of CAVR, we describe two approaches that exploit word embedding techniques to infer the vector-space representations of venues, users’ existing preferences, and users’ contextual preferences. Our evaluation upon the test collection of the TREC 2015 Contextual Suggestion track demonstrates that we can significantly enhance the effectiveness of a state-of-the-art venue recommendation approach, as well as produce context-aware recommendations that are at least as effective as the top TREC 2015 systems

Supervised extractive summarisation of news events

This thesis investigates whether the summarisation of news-worthy events can be improved by using evidence about entities (i.e.\ people, places, and organisations) involved in the events. More effective event summaries, that better assist people with their news-based information access requirements, can help to reduce information overload in today's 24-hour news culture. Summaries are based on sentences extracted verbatim from news articles about the events. Within a supervised machine learning framework, we propose a series of entity-focused event summarisation features. Computed over multiple news articles discussing a given event, such entity-focused evidence estimates: the importance of entities within events; the significance of interactions between entities within events; and the topical relevance of entities to events. The statement of this research work is that augmenting supervised summarisation models, which are trained on discriminative multi-document newswire summarisation features, with evidence about the named entities involved in the events, by integrating entity-focused event summarisation features, we will obtain more effective summaries of news-worthy events. The proposed entity-focused event summarisation features are thoroughly evaluated over two multi-document newswire summarisation scenarios. The first scenario is used to evaluate the retrospective event summarisation task, where the goal is to summarise an event to-date, based on a static set of news articles discussing the event. The second scenario is used to evaluate the temporal event summarisation task, where the goal is to summarise the changes in an ongoing event, based on a time-stamped stream of news articles discussing the event. The contributions of this thesis are two-fold. First, this thesis investigates the utility of entity-focused event evidence for identifying important and salient event summary sentences, and as a means to perform anti-redundancy filtering to control the volume of content emitted as a summary of an evolving event. Second, this thesis also investigates the validity of automatic summarisation evaluation metrics, the effectiveness of standard summarisation baselines, and the effective training of supervised machine learned summarisation models

Filtering News from Document Streams: Evaluation Aspects and Modeled Stream Utility

Events like hurricanes, earthquakes, or accidents can impact a large number of people. Not only are people in the immediate vicinity of the event affected, but concerns about their well-being are shared by the local government and well-wishers across the world. The latest information about news events could be of use to government and aid agencies in order to make informed decisions on providing necessary support, security and relief. The general public avails of news updates via dedicated news feeds or broadcasts, and lately, via social media services like Facebook or Twitter. Retrieving the latest information about newsworthy events from the world-wide web is thus of importance to a large section of society. As new content on a multitude of topics is continuously being published on the web, specific event related information needs to be filtered from the resulting stream of documents. We present in this thesis, a user-centric evaluation measure for evaluating systems that filter news related information from document streams. Our proposed evaluation measure, Modeled Stream Utility (MSU), models users accessing information from a stream of sentences produced by a news update filtering system. The user model allows for simulating a large number of users with different characteristic stream browsing behavior. Through simulation, MSU estimates the utility of a system for an average user browsing a stream of sentences. Our results show that system performance is sensitive to a user population's stream browsing behavior and that existing evaluation metrics correspond to very specific types of user behavior. To evaluate systems that filter sentences from a document stream, we need a set of judged sentences. This judged set is a subset of all the sentences returned by all systems, and is typically constructed by pooling together the highest quality sentences, as determined by respective system assigned scores for each sentence. Sentences in the pool are manually assessed and the resulting set of judged sentences is then used to compute system performance metrics. In this thesis, we investigate the effect of including duplicates of judged sentences, into the judged set, on system performance evaluation. We also develop an alternative pooling methodology, that given the MSU user model, selects sentences for pooling based on the probability of a sentences being read by modeled users. Our research lays the foundation for interesting future work for utilizing user-models in different aspects of evaluation of stream filtering systems. The MSU measure enables incorporation of different user models. Furthermore, the applicability of MSU could be extended through calibration based on user behavior

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Spoken content retrieval beyond pipeline integration of automatic speech recognition and information retrieval

The dramatic increase in the creation of multimedia content is leading to the development of large archives in which a substantial amount of the information is in spoken form. Efficient access to this information requires effective spoken content retrieval (SCR) methods. Traditionally, SCR systems have focused on a pipeline integration of two fundamental technologies: transcription using automatic speech recognition (ASR) and search supported using text-based information retrieval (IR). Existing SCR approaches estimate the relevance of a spoken retrieval item based on the lexical overlap between a user’s query and the textual transcriptions of the items. However, the speech signal contains other potentially valuable non-lexical information that remains largely unexploited by SCR approaches. Particularly, acoustic correlates of speech prosody, that have been shown useful to identify salient words and determine topic changes, have not been exploited by existing SCR approaches. In addition, the temporal nature of multimedia content means that accessing content is a user intensive, time consuming process. In order to minimise user effort in locating relevant content, SCR systems could suggest playback points in retrieved content indicating the locations where the system believes relevant information may be found. This typically requires adopting a segmentation mechanism for splitting documents into smaller “elements” to be ranked and from which suitable playback points could be selected. Existing segmentation approaches do not generalise well to every possible information need or provide robustness to ASR errors. This thesis extends SCR beyond the standard ASR and IR pipeline approach by: (i) exploring the utilisation of prosodic information as complementary evidence of topical relevance to enhance current SCR approaches; (ii) determining elements of content that, when retrieved, minimise user search effort and provide increased robustness to ASR errors; and (iii) developing enhanced evaluation measures that could better capture the factors that affect user satisfaction in SCR

Filtrage et agrégation d'informations vitales relatives à des entités

Nowadays, knowledge bases such as Wikipedia and DBpedia are the main sources to access information on a wide variety of entities (an entity is a thing that can be distinctly identified such a person, an organization, a product, an event, etc.). However, the update of these sources with new information related to a given entity is done manually by contributors with a significant latency time particularly if that entity is not popular. A system that analyzes documents when published on the Web to filter important information about entities will probably accelerate the update of these knowledge bases. In this thesis, we are interested in filtering timely and relevant information, called vital information, concerning the entities. We aim at answering the following two issues: (1) How to detect if a document is vital (i.e., it provides timely relevant information) to an entity? and (2) How to extract vital information from these documents to build a temporal summary about the entity that can be seen as a reference for updating the corresponding knowledge base entry?Regarding the first issue, we proposed two methods. The first proposal is fully supervised. It is based on a vitality language model. The second proposal measures the freshness of temporal expressions in a document to decide its vitality. Concerning the second issue, we proposed a method that selects the sentences based on the presence of triggers words automatically retrieved from the knowledge already represented in the knowledge base (such as the description of similar entities).We carried out our experiments on the TREC Stream corpus 2013 and 2014 with 1.2 billion documents and different types of entities (persons, organizations, facilities and events). For vital documents filtering approaches, we conducted our experiments in the context of the task "knowledge Base Acceleration (KBA)" for the years 2013 and 2014. Our method based on leveraging the temporal expressions in the document obtained good results outperforming the best participant system in the task KBA 2013. In addition, we showed the importance of our generated temporal summaries to accelerate the update of knowledge bases.Aujourd'hui, les bases de connaissances telles que Wikipedia et DBpedia représentent les sources principales pour accéder aux informations disponibles sur une grande variété d'entités (une entité est une chose qui peut être distinctement identifiée par exemple une personne, une organisation, un produit, un événement, etc.). Cependant, la mise à jour de ces sources avec des informations nouvelles en rapport avec une entité donnée se fait manuellement par des contributeurs et avec un temps de latence important en particulier si cette entité n'est pas populaire. Concevoir un système qui analyse les documents dès leur publication sur le Web pour filtrer les informations importantes relatives à des entités pourra sans doute accélérer la mise à jour de ces bases de connaissances. Dans cette thèse, nous nous intéressons au filtrage d'informations pertinentes et nouvelles, appelées vitales, relatives à des entités. Ces travaux rentrent dans le cadre de la recherche d'information mais visent aussi à enrichir les techniques d'ingénierie de connaissances en aidant à la sélection des informations à traiter. Nous souhaitons répondre principalement aux deux problématiques suivantes: (1) Comment détecter si un document est vital (c.à.d qu'il apporte une information pertinente et nouvelle) par rapport à une entité donnée? et (2) Comment extraire les informations vitales à partir de ces documents qui serviront comme référence pour mettre à jour des bases de connaissances? Concernant la première problématique, nous avons proposé deux méthodes. La première proposition est totalement supervisée. Elle se base sur un modèle de langue de vitalité. La deuxième proposition mesure la fraîcheur des expressions temporelles contenues dans un document afin de décider de sa vitalité. En ce qui concerne la deuxième problématique relative à l'extraction d'informations vitales à partir des documents vitaux, nous avons proposé une méthode qui sélectionne les phrases comportant potentiellement ces informations vitales, en nous basant sur la présence de mots déclencheurs récupérés automatiquement à partir de la connaissance déjà représentée dans la base de connaissances (comme la description d'entités similaires).L'évaluation des approches proposées a été effectuée dans le cadre de la campagne d'évaluation internationale TREC sur une collection de 1.2 milliard de documents avec différents types d'entités (personnes, organisations, établissements et événements). Pour les approches de filtrage de documents vitaux, nous avons mené nos expérimentations dans le cadre de la tâche "Knwoledge Base Acceleration (KBA)" pour les années 2013 et 2014. L'exploitation des expressions temporelles dans le document a permis d'obtenir de bons résultats dépassant le meilleur système proposé dans la tâche KBA 2013. Pour évaluer les contributions concernant l'extraction des informations vitales relatives à des entités, nous nous sommes basés sur le cadre expérimental de la tâche "Temporal Summarization (TS)". Nous avons montré que notre approche permet de minimiser le temps de latence des mises à jour de bases de connaissances

Modelling search and stopping in interactive information retrieval

Searching for information when using a computerised retrieval system is a complex and inherently interactive process. Individuals during a search session may issue multiple queries, and examine a varying number of result summaries and documents per query. Searchers must also decide when to stop assessing content for relevance - or decide when to stop their search session altogether. Despite being such a fundamental activity, only a limited number of studies have explored stopping behaviours in detail, with a majority reporting that searchers stop because they decide that what they have found feels "good enough". Notwithstanding the limited exploration of stopping during search, the phenomenon is central to the study of Information Retrieval, playing a role in the models and measures that we employ. However, the current de facto assumption considers that searchers will examine k documents - examining up to a fixed depth. In this thesis, we examine searcher stopping behaviours under a number of different search contexts. We conduct and report on two user studies, examining how result summary lengths and a variation of search tasks and goals affect such behaviours. Interaction data from these studies are then used to ground extensive simulations of interaction, exploring a number of different stopping heuristics (operationalised as twelve stopping strategies). We consider how well the proposed strategies perform and match up with real-world stopping behaviours. As part of our contribution, we also propose the Complex Searcher Model, a high-level conceptual searcher model that encodes stopping behaviours at different points throughout the search process. Within the Complex Searcher Model, we also propose a new results page stopping decision point. From this new stopping decision point, searchers can obtain an impression of the page before deciding to enter or abandon it. Results presented and discussed demonstrate that searchers employ a range of different stopping strategies, with no strategy standing out in terms of performance and approximations offered. Stopping behaviours are clearly not fixed, but are rather adaptive in nature. This complex picture reinforces the idea that modelling stopping behaviour is difficult. However, simplistic stopping strategies do offer good performance and approximations, such as the frustration-based stopping strategy. This strategy considers a searcher's tolerance to non-relevance. We also find that combination strategies - such as those combining a searcher's satisfaction with finding relevant material, and their frustration towards observing non-relevant material - also consistently offer good approximations and performance. In addition, we also demonstrate that the inclusion of the additional stopping decision point within the Complex Searcher Model provides significant improvements to performance over our baseline implementation. It also offers improvements to the approximations of real-world searcher stopping behaviours. This work motivates a revision of how we currently model the search process and demonstrates that different stopping heuristics need to be considered within the models and measures that we use in Information Retrieval. Measures should be reformed according to the stopping behaviours of searchers. A number of potential avenues for future exploration can also be considered, such as modelling the stopping behaviours of searchers individually (rather than as a population), and to explore and consider a wider variety of different stopping heuristics under different search contexts. Despite the inherently difficult task that understanding and modelling the stopping behaviours of searchers represents, potential benefits of further exploration in this area will undoubtedly aid the searchers of future retrieval systems - with further work bringing about improved interfaces and experiences