Privacy-enabled scalable recommender systems

Electronic content is ubiquitous in our daily lives. Several factors such as the development of Web 2.0 technologies, the increased access to mobile devices and the deployment of mobile networks has undoubtedly augmented the amount of information easily available to users. Given the limited attentio...

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Autores:: Moreno Barbosa, Andrés Darío

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2015

Institución:: Universidad de los Andes

Repositorio:: Séneca: repositorio Uniandes

Idioma:: eng

Description
Summary:	Electronic content is ubiquitous in our daily lives. Several factors such as the development of Web 2.0 technologies, the increased access to mobile devices and the deployment of mobile networks has undoubtedly augmented the amount of information easily available to users. Given the limited attention span of the user and the extensiveness of the available streams of information ready to be consumed, automatic systems must be available for the user to prioritize, suggest or screen content suitable for the user interests and situation. One of the most popular initiatives created to solve the information overload problem are Recommender Systems [Adomavicius 2005]. Recommender Systems are information filtering systems that use the historical information about the user (what the user has considered relevant or irrelevant on the past, among other information) to build and accurate representation of the user's interests that is used to predict the relevance of a large collection of available items for a specific user. Recommendation systems are used by several online retailers, online content streaming services and social networking sites to improve the user's experience of their services by automatically filtering their content or offers of items to the ones most likely to interest the user. Generally speaking, recommender systems can be classified into two categories: Content Based and Collaborative Filtering. The former category relies on the definition of explicit features that describe the item domain and assigns them weights to describe the affinity between the feature and the item. For example in the movie domain, items can be described by features such as the genre to which they belong, the director, writer and actors that take part in the movie. On the other hand Collaborative Filtering is content-agnostic and relies on correlations between users and items based on the historical consumption patterns between the users and the items. It has been shown generally that Collaborative Filtering methods present better results than Content-Based [Pil'aszy 2009], however due to inherent shortcomings of single approaches, a better predictive performance is achieved by developing a model that integrates different paradigms (Hybrid approaches). To keep their users satisfied, personalization services that operate recommendation methods should present relevant recommendations even when the number of users, items and user-item interactions in the system increase

Privacy-enabled scalable recommender systems

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