Similarity and diversity: two sides of the same coin in the evaluation of data streams

The Information Systems represent the primary instrument of growth for the companies that operate in the so-called e-commerce environment. The data streams generated by the users that interact with their websites are the primary source to define the user behavioral models. Some main examples of services integrated in these websites are the Recommender Systems, where these models are exploited in order to generate recommendations of items of potential interest to users, the User Segmentation Systems, where the models are used in order to group the users on the basis of their preferences, and the Fraud Detection Systems, where these models are exploited to determine the legitimacy of a financial transaction. Even though in literature diversity and similarity are considered as two sides of the same coin, almost all the approaches take into account them in a mutually exclusive manner, rather than jointly. The aim of this thesis is to demonstrate how the consideration of both sides of this coin is instead essential to overcome some well-known problems that affict the state-of-the-art approaches used to implement these services, improving their performance. Its contributions are the following: with regard to the recommender systems, the detection of the diversity in a user profile is used to discard incoherent items, improving the accuracy, while the exploitation of the similarity of the predicted items is used to re-rank the recommendations, improving their effectiveness; with regard to the user segmentation systems, the detection of the diversity overcomes the problem of the non-reliability of data source, while the exploitation of the similarity reduces the problems of understandability and triviality of the obtained segments; lastly, concerning the fraud detection systems, the joint use of both diversity and similarity in the evaluation of a new transaction overcomes the problems of the data scarcity, and those of the non-stationary and unbalanced class distribution

Similarity and diversity: two sides of the same coin in the evaluation of data streams

The Information Systems represent the primary instrument of growth for the companies that operate in the so-called e-commerce environment. The data streams generated by the users that interact with their websites are the primary source to define the user behavioral models. Some main examples of services integrated in these websites are the Recommender Systems, where these models are exploited in order to generate recommendations of items of potential interest to users, the User Segmentation Systems, where the models are used in order to group the users on the basis of their preferences, and the Fraud Detection Systems, where these models are exploited to determine the legitimacy of a financial transaction. Even though in literature diversity and similarity are considered as two sides of the same coin, almost all the approaches take into account them in a mutually exclusive manner, rather than jointly. The aim of this thesis is to demonstrate how the consideration of both sides of this coin is instead essential to overcome some well-known problems that affict the state-of-the-art approaches used to implement these services, improving their performance. Its contributions are the following: with regard to the recommender systems, the detection of the diversity in a user profile is used to discard incoherent items, improving the accuracy, while the exploitation of the similarity of the predicted items is used to re-rank the recommendations, improving their effectiveness; with regard to the user segmentation systems, the detection of the diversity overcomes the problem of the non-reliability of data source, while the exploitation of the similarity reduces the problems of understandability and triviality of the obtained segments; lastly, concerning the fraud detection systems, the joint use of both diversity and similarity in the evaluation of a new transaction overcomes the problems of the data scarcity, and those of the non-stationary and unbalanced class distribution

Recommendation in Enterprise 2.0 Social Media Streams

A social media stream allows users to share user-generated content as well as aggregate different external sources into one single stream. In Enterprise 2.0 such social media streams empower co-workers to share their information and to work efficiently and effectively together while replacing email communication. As more users share information it becomes impossible to read the complete stream leading to an information overload. Therefore, it is crucial to provide the users a personalized stream that suggests important and unread messages. The main characteristic of an Enterprise 2.0 social media stream is that co-workers work together on projects represented by topics: the stream is topic-centered and not user-centered as in public streams such as Facebook or Twitter. A lot of work has been done dealing with recommendation in a stream or for news recommendation. However, none of the current research approaches deal with the characteristics of an Enterprise 2.0 social media stream to recommend messages. The existing systems described in the research mainly deal with news recommendation for public streams and lack the applicability for Enterprise 2.0 social media streams. In this thesis a recommender concept is developed that allows the recommendation of messages in an Enterprise 2.0 social media stream. The basic idea is to extract features from a new message and use those features to compute a relevance score for a user. Additionally, those features are used to learn a user model and then use the user model for scoring new messages. This idea works without using explicit user feedback and assures a high user acceptance because no intense rating of messages is necessary. With this idea a content-based and collaborative-based approach is developed. To reflect the topic-centered streams a topic-specific user model is introduced which learns a user model independently for each topic. There are constantly new terms that occur in the stream of messages. For improving the quality of the recommendation (by finding more relevant messages) the recommender should be able to handle the new terms. Therefore, an approach is developed which adapts a user model if unknown terms occur by using terms of similar users or topics. Also, a short- and long-term approach is developed which tries to detect short-term interests of users. Only if the interest of a user occurs repeatedly over a certain time span are terms transferred to the long-term user model. The approaches are evaluated against a dataset obtained through an Enterprise 2.0 social media stream application. The evaluation shows the overall applicability of the concept. Specifically the evaluation shows that a topic-specific user model outperforms a global user model and also that adapting the user model according to similar users leads to an increase in the quality of the recommendation. Interestingly, the collaborative-based approach cannot reach the quality of the content-based approach

Enhancing Recommendations in Specialist Search Through Semantic-based Techniques and Multiple Resources

Information resources abound on the Internet, but mining these resources is a non-trivial task. Such abundance has raised the need to enhance services provided to users, such as recommendations. The purpose of this work is to explore how better recommendations can be provided to specialists in specific domains such as bioinformatics by introducing semantic techniques that reason through different resources and using specialist search techniques. Such techniques exploit semantic relations and hidden associations that occur as a result of the information overlapping among various concepts in multiple bioinformatics resources such as ontologies, websites and corpora. Thus, this work introduces a new method that reasons over different bioinformatics resources and then discovers and exploits different relations and information that may not exist in the original resources. Such relations may be discovered as a consequence of the information overlapping, such as the sibling and semantic similarity relations, to enhance the accuracy of the recommendations provided on bioinformatics content (e.g. articles). In addition, this research introduces a set of semantic rules that are able to extract different semantic information and relations inferred among various bioinformatics resources. This project introduces these semantic-based methods as part of a recommendation service within a content-based system. Moreover, it uses specialists' interests to enhance the provided recommendations by employing a method that is collecting user data implicitly. Then, it represents the data as adaptive ontological user profiles for each user based on his/her preferences, which contributes to more accurate recommendations provided to each specialist in the field of bioinformatics

Exploratory Browsing

In recent years the digital media has influenced many areas of our life. The transition from analogue to digital has substantially changed our ways of dealing with media collections. Today‟s interfaces for managing digital media mainly offer fixed linear models corresponding to the underlying technical concepts (folders, events, albums, etc.), or the metaphors borrowed from the analogue counterparts (e.g., stacks, film rolls). However, people‟s mental interpretations of their media collections often go beyond the scope of linear scan. Besides explicit search with specific goals, current interfaces can not sufficiently support the explorative and often non-linear behavior. This dissertation presents an exploration of interface design to enhance the browsing experience with media collections. The main outcome of this thesis is a new model of Exploratory Browsing to guide the design of interfaces to support the full range of browsing activities, especially the Exploratory Browsing. We define Exploratory Browsing as the behavior when the user is uncertain about her or his targets and needs to discover areas of interest (exploratory), in which she or he can explore in detail and possibly find some acceptable items (browsing). According to the browsing objectives, we group browsing activities into three categories: Search Browsing, General Purpose Browsing and Serendipitous Browsing. In the context of this thesis, Exploratory Browsing refers to the latter two browsing activities, which goes beyond explicit search with specific objectives. We systematically explore the design space of interfaces to support the Exploratory Browsing experience. Applying the methodology of User-Centered Design, we develop eight prototypes, covering two main usage contexts of browsing with personal collections and in online communities. The main studied media types are photographs and music. The main contribution of this thesis lies in deepening the understanding of how people‟s exploratory behavior has an impact on the interface design. This thesis contributes to the field of interface design for media collections in several aspects. With the goal to inform the interface design to support the Exploratory Browsing experience with media collections, we present a model of Exploratory Browsing, covering the full range of exploratory activities around media collections. We investigate this model in different usage contexts and develop eight prototypes. The substantial implications gathered during the development and evaluation of these prototypes inform the further refinement of our model: We uncover the underlying transitional relations between browsing activities and discover several stimulators to encourage a fluid and effective activity transition. Based on this model, we propose a catalogue of general interface characteristics, and employ this catalogue as criteria to analyze the effectiveness of our prototypes. We also present several general suggestions for designing interfaces for media collections

Mobile App Recommendation

Ph.DDOCTOR OF PHILOSOPH

A semantic web service-based framework for generic personalization and user modeling

[no abstract

Integration of a recommender system into an online video streaming platform

The ultimate goal of this project is to develop a recommender system for the SmartVideo platform. The platform streams different content of local channels for the Grand Est Region of France to a large public. So, we aim to propose a solution to alleviate the data representation and data collection issue of recommender systems by adopting and adjusting the xAPI standard to fit our case of study and to be able to represent our usage data in a formal and consistent format. Then, we will propose and implement a bunch of recommendation algorithms that we are going to test in order to evaluate our developed recommender system.Le but ultime de ce projet est de développer un système de recommandation dédié à la plateforme SmartVideo de diffusion de vidéo en ligne. En effet, la plateforme met à disposition diverses contenus des chaînes locales de la région Grand Est du France. Alors, nous allons présenter une solution pour alléger le problème de représentation et de collecte de données d’usages par adopter et ajuster le standard xAPI pour représenter et collecter les données de façon simple et formelle. Ensuite, nous allons proposer et implanter des algorithmes de recommandation que nous allons les tester pour évaluer notre système de recommandation

Self-Organizing Teams in Online Work Settings

As the volume and complexity of distributed online work increases, the collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of such teams by grouping workers according to a set of predefined decision criteria. This approach micro-manages workers, who have no say in the team formation process. Depriving users of control over who they will work with stifles creativity, causes psychological discomfort and results in less-than-optimal collaboration results. In this work, we propose an alternative model, called Self-Organizing Teams (SOTs), which relies on the crowd of online workers itself to organize into effective teams. Supported but not guided by an algorithm, SOTs are a new human-centered computational structure, which enables participants to control, correct and guide the output of their collaboration as a collective. Experimental results, comparing SOTs to two benchmarks that do not offer user agency over the collaboration, reveal that participants in the SOTs condition produce results of higher quality and report higher teamwork satisfaction. We also find that, similarly to machine learning-based self-organization, human SOTs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible teammates