Exploring an opinion network for taste prediction: an empirical study

We develop a simple statistical method to find affinity relations in a large opinion network which is represented by a very sparse matrix. These relations allow us to predict missing matrix elements. We test our method on the Eachmovie data of thousands of movies and viewers. We found that significant prediction precision can be achieved and it is rather stable. There is an intrinsic limit to further improve the prediction precision by collecting more data, implying perfect prediction can never obtain via statistical means.Comment: 9 pages, 4 figure

Effect of initial configuration on network-based recommendation

In this paper, based on a weighted object network, we propose a recommendation algorithm, which is sensitive to the configuration of initial resource distribution. Even under the simplest case with binary resource, the current algorithm has remarkably higher accuracy than the widely applied global ranking method and collaborative filtering. Furthermore, we introduce a free parameter $\beta$ to regulate the initial configuration of resource. The numerical results indicate that decreasing the initial resource located on popular objects can further improve the algorithmic accuracy. More significantly, we argue that a better algorithm should simultaneously have higher accuracy and be more personal. According to a newly proposed measure about the degree of personalization, we demonstrate that a degree-dependent initial configuration can outperform the uniform case for both accuracy and personalization strength.Comment: 4 pages and 3 figure

A Framework for Interaction-driven User Modeling of Mobile News Reading Behaviour

The news you read is, of course, a highly individual choice and one for which substantial and successful news recommendation techniques have been developed. But as well as what news you read, the way you choose and read that news is also known to be highly individual. We propose a framework for extending the user profile of news readers with features of these interactions. The extensions are dynamic through monitoring an individual's reading and browsing activity. They include factors learned from the user's interaction log and also factors inferred from category level definitions contained in the framework. We report a study in which users' interaction logs with a news app are used to generate user profiles that are verified with self-reported questionnaire data about reading habits. We discuss the implications of our user modeling approach in news personalisation for both recommendation and user interface personalisation for news apps

Emergence of scale-free leadership structure in social recommender systems

The study of the organization of social networks is important for understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a "good get richer" mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Time-Sensitive User Profile for Optimizing Search Personlization

International audienceThanks to social Web services, Web search engines have the opportunity to afford personalized search results that better fit the user’s information needs and interests. To achieve this goal, many personalized search approaches explore user’s social Web interactions to extract his preferences and interests, and use them to model his profile. In our approach, the user profile is implicitly represented as a vector of weighted terms which correspond to the user’s interests extracted from his online social activities. As the user interests may change over time, we propose to weight profiles terms not only according to the content of these activities but also by considering the freshness. More precisely, the weights are adjusted with a temporal feature. In order to evaluate our approach, we model the user profile according to data collected from Twitter. Then, we rerank initial search results accurately to the user profile. Moreover, we proved the significance of adding a temporal feature by comparing our method with baselines models that does not consider the user profile dynamics

Places for News:A Situated Study of Context in News Consumption

This paper presents a qualitative study of contextual factors that affect news consumption on mobile devices. Participants reported their daily news consumption activities over a period of two weeks through a snippet-based diary and experience sampling study, followed by semi-structured exit interviews. Wunderlist, a commercially available task management application and note-taking software, was appropriated for data collection. Findings highlighted a range of contextual factors that are not accounted for in current ‘contextually-aware’ news delivery technologies, and could be developed to better adapt such technologies in the future. These contextual factors were segmented to four areas: triggers, positive/conducive factors, negative/distracting factors and barriers to use