Abstract

In a Web dominated by social media for information, relationships and communication, the established dynamics between content, people and technology change radically. Given the relevance of the user-generated content in such a scenario and its essentially relational nature, successfully locating the best sources of information requires the development of recommender systems that incorporate the social characteristic of scenarios built on a network that goes beyond the original Internet. The article provides a review of current approaches to the process of recommendation, placing them in the context of consolidated trends associated with the phenomenon of Social Computing, and highlights some lines of development in the area of redefining the problem of recommendation in a context dominated by social networks and user-generated content.

Abstract

We present the theoretical and methodological foundations for the development of a multi-agent SDI service model for specialized digital libraries, applying semantic web technologies that permit more efficient information management, improving agent-user communication processes and facilitating accurate access to relevant resources. To do this, rss feeds are used as “current awareness bulletins” to generate personalized bibliographic alerts. The SDI service model has an rss feeds management module and an information push module. In the first module, resources are represented as rss feed items and are also semi-automatically assigned subject terms by matching their associated keywords against the terms of a SKOS Core format thesaurus. In the information push module, bibliographic alerts are customized according to the preferences defined on users’ profiles.

Abstract

In the current techno-social environment the importance of providing personalized content for users of social platforms, continually adapting to their needs, is key to the success of the existing platforms. Recommender systems play a crucial role in this regard, but in many cases the level of customization provided is not enough. This article discusses two key aspects related to the development of recommender systems: proactivity and context-awareness. We propose a theoretical reference model for the creation of proactive recommender systems based on context-awareness information. In addition, we check its feasibility in two real-world scenarios where the model has been successfully implemented: banking and personal learning networks. Finally, future courses of action following the contributions proposed are highlighted, with special attention to potential applications in educational environments.

Abstract

Rapid adoption of smartphones and the business success of the Apple App Store have resulted in the rampant growth of mobile applications. Seeking new revenue opportunities from application development has created a gold rush. However, free or very cheap applications constitute a great bulk of the application downloads putting great pricing pressure on the developers. Furthermore, usage statistics suggest that most of the applications have been either one-trick applications or are downright useless, meriting no attention from the user beyond the first day. This is not surprising since cheap prices will dissuade developers from investing large sums of money to continue to develop more sophisticated, high quality applications. Developers have been complaining about the lack of visibility of their applications in stores that are beginning to resemble a high volume warehouse. It is clear that enhancing application discovery and building better marketing tools will be essential for the continued success of the mobile application marketplace and application stores. This paper proposes and investigates techniques for effective discovery of applications by matching user interests with application characteristics, with a special focus on adapting classical data mining techniques to user ratings of the applications. The user ratings are leveraged to make recommendations on potential applications of interest. Publisher's Version

Abstract

city offers thousands of social events a day, and it is difficult for dwellers to make choices. The combination of mobile phones and recommender systems can change the way one deals with such abundance. Mobile phones with positioning technology are now widely available, making it easy for people to broadcast their whereabouts, recommender systems can now identify patterns in people’s movements in order to, for example, recommend events. To do so, the system relies on having mobile users who share their attendance at a large number of social events: cold-start users, who have no location history, cannot receive recommendations. We set out to address the mobile cold-start problem by answering the following research question: how can social events be recommended to a cold-start user based only on his home location? To answer this question, we carry out a study of the relationship between preferences for social events and geography, the first of its kind in a large metropolitan area. We sample location estimations of one million mobile phone users in Greater Boston, combine the sample with social events in the same area, and infer the social events attended by 2,519 residents. Upon this data, we test a variety of algorithms for recommending social events. We find that the most effective algorithm recommends events that are popular among residents of an area. The least effective, instead, recommends events that are geographically close to the area. This last result has interesting implications for location-based services that emphasize recommending nearby events.