Exposure diversity as a design principle for recommender systems

Personalized recommendations in search engines, social media and also in more traditional media increasingly raise concerns over potentially negative consequences for diversity and the quality of public discourse. The algorithmic filtering and adaption of online content to personal preferences and interests is often associated with a decrease in...

Impact of Social Media on TV Content Consumption: New Market Strategies, Scenarios and Trends

The mass adoption of Social Media together with the proliferation and widely usage of multi-connected companion devices have tremendously transformed the TV/video consumption paradigm, opening the door to a new range of possibilities. This Special Issue has aimed at analyzing, from different point of views, the impact of Social Media and social...

Recommending personalized touristic sights using Google Places

The purpose of the Contextual Suggestion track, an evaluation task at the TREC 2012 conference, is to suggest personalized tourist activities to an individual, given a certain location and time. In our content-based approach, we collected initial recommendations using the location context as search query in Google Places. We first ranked the...

Group recommendation in an hybrid broadcast broadband television context

This paper presents insights and learning experiences on the development of an integrated group recommender system in the European FP7 HBBNext research project. The system design incorporates insights from user research and evaluations, media industry players, and European HbbTV standardization efforts. Important differences were found between...

Generating private recommendations efficiently using homomorphic encryption and data packing

Recommender systems have become an important tool for personalization of online services. Generating recommendations in online services depends on privacy-sensitive data collected from the users. Traditional data protection mechanisms focus on access control and secure transmission, which provide security only against malicious third parties,...

Privacy-Preserving Content-Based Recommender System

By offering personalized content to users, recommender systems have become a vital tool in e-commerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing ratings, on which content-based methods rely,...

Making the news interesting: Understanding the relationship between familiarity and interest

News feeds are an important element of information encountering, feeding our (new) interests but also leading to a state of information overload. Current solutions often select information similar to the user's interests. However, long-term interest in one topic, and being highly familiar with that topic, does not necessarily imply an actual...

Privacy-Preserving Content-Based Recommendations through Homomorphic Encryption

By offering personalized content to users, recommender systems have become a vital tool in ecommerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing ratings, on which content-based methods rely,...

Generating private recommendations in a social trust network

Recommender systems have become increasingly important in e-commerce as they can guide customers with finding personalized services and products. A variant of recommender systems that generates recommendations from a set of trusted people is recently getting more attention in social networks. However, people are concerned about their privacy as...

Efficiently Computing Private Recommendations

Online recommender systems enable personalized service to users. The underlying collaborative filtering techniques operate on privacy sensitive user data, which could be misused by the service provider. To protect user privacy, we propose to encrypt the data and generate recommendations by processing them under encryption. Thus, the service...

Privacy Enhanced Recommender System

Recommender systems are widely used in online applications since they enable personalized service to the users. The underlying collaborative filtering techniques work on user’s data which are mostly privacy sensitive and can be misused by the service provider. To protect the privacy of the users, we propose to encrypt the privacy sensitive data...

Efficiently Computing Private Recommendations

Online recommender systems enable personalized service to users. The underlying collaborative filtering techniques operate on privacy sensitive user data, which could be misused if it is leaked or by the service provider him self. To protect user’s privacy, we propose to encrypt the data and generate recommendations by processing them under...

The seniorgezond recommender: Exploration put into practice

Recommender systems suggest objects to users navigating a web site. They observe the pages that a user visits and predict which other pages may be of interest. On the basis of these predictions recommenders select a number of pages that are suggested to the user. By far the most popular recommendation strategy is to select the pages of which the...