A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

Three-dimensional context-aware tailoring of information

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2010 EmeraldPurpose – The purpose of this paper is to explore the notion of context in ubiquitous computing. Personal Information Managers exploit the ubiquitous paradigm in mobile computing to integrate services and programs for business and leisure. Recognising that every situation is constituted by information and events, context will vary depending on the situation in which users find themselves. The paper aims to show the viability of tailoring contextual information to provide users with timely and relevant information. Design/methodology/approach – A survey was conducted after testing on a group of real world users. The test group used the application for approximately half a day each and performed a number of tasks. Findings – The results from the survey show the viability of tailoring contextual information to provide users with timely and relevant information. Among the questions in the questionnaire the users were asked to state whether or not they would like to use this application in their daily life. Statistically significant results indicate that the users found value in using the application. Originality/value – This work is a new exploration and implementation of context by integrating three dimensions of context: social information, activity information, and geographical position

Rule-based Reasoning Mechanism for Context-aware Service Presentation

With universal usability geared towards user focused customisation, a context reasoning engine can derive meaning from the various context elements and facilitate decision-taking for applications and context delivery mechanisms. The heterogeneity of available device capabilities means that the recommendation algorithm must be in a formal, effective and extensible form. Moreover, user preferences, capability context and media metadata must be considered simultaneously to determine appropriate presentation format. Towards this aim, this paper presents a reasoning mechanism that supports service presentation through a rule-based mechanism. The validation of the approach is presented through application use cases

A contextual modeling approach for model-based recommender systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40643-0_5Proceedings of 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013.In this paper we present a contextual modeling approach for model-based recommender systems that integrates and exploits both user preferences and contextual signals in a common vector space. Differently to previous work, we conduct a user study acquiring and analyzing a variety of realistic contextual signals associated to user preferences in several domains. Moreover, we report empirical results evaluating our approach in the movie and music domains, which show that enhancing model-based recommender systems with time, location and social companion information improves the accuracy of generated recommendations

A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations

Information Entropy Theory Based Recognition of the Validity of Contextual Information of Restaurants: An Empirical Study

Contextual information plays a key role in personalized recommendations. However, not all contextual information plays a positive role in personalized recommendations. Therefore, it is critical to identify the effective contextual information to realize personalized recommendations. This study aims to develop a set of feasible context importance calculation methods that can identify effective contextual information in different application scenarios. The information entropy of each contextual dimension is calculated, and the validity of the context compared according to the magnitude of its entropy is determined based on the informational entropy theory. Subsequently, this approach is applied to hotel and catering service data to determine the valid context in the dining domain. The experimental results indicate that location, work-rest condition, weather, mood and companionship considerably influence consumers’ behaviour and decisions in a catering environment, and the user preference in such contexts should be carefully considered

A Framework for Exploiting Internet of Things for Context-Aware Trust-based Personalized Services

In the last years, we have witnessed the introduction of Internet of Things as an integral part of the Internet with billions of interconnected and addressable everyday objects. On the one hand, these objects generate massive volume of data that can be exploited to gain useful insights into our day-to-day needs. On the other hand, context-aware recommender systems (CARSs) are intelligent systems that assist users to make service consumption choices that satisfy their preferences based on their contextual situations. However, one of the major challenges in developing CARSs is the lack of functionality providing dynamic and reliable context information required by the recommendation decision process based on the objects that users interact with in their environments. Thus, contextual information obtained from IoT objects and other sources can be exploited to build CARSs that satisfy users’ preferences, improve quality of experience and recommendation accuracy. This article describes various components of a conceptual IoT based framework for context-aware personalized recommendations. The framework addresses the weakness whereby CARSs rely on static and limited contextual information from user’s mobile phone, by providing additional components for reliable and dynamic contextual information, using IoT context sources. The core of the framework consists of context recognition and reasoning management, dynamic user profile model incorporating trust to improve accuracy of context-aware personalized recommendations. Experimental evaluations show that incorporating context and trust in personalized recommendations can improve its accuracy

Personalized And Situation-Aware Recommendations For Runners

The project uService investigates the transformation of a mobile user into a service super prosumer, i.e., a producer, provider and consumer of services at the same time. The goal is to develop a platform which enables a user to create, discover and consume mobile services anywhere and at any time on the mobile device. uRun is an application scenario of the project in the field of mobile health and fitness. The uRun framework provides a mobile assistance system particularly for runners, which combines Web 2.0 and Web 3.0 technologies and personalized and situation-aware recommendation mechanisms. The ability to create individual and mobile health and fitness services as well as a personalized and situation-aware assistance system based on a semantic knowledge base are considered to provide an edge over existing consumer-centric health care systems. In this article, we describe the recommendation mechanism and the incorporation of semantic knowledge for the uService platform and the uRun framework