Aspect-Driven User Preference and News Representation Learning for News Recommendation

Context-Aware User-Driven News Recommendation

Aspect-Driven News Summarization

A summary of any event type is only complete if certain information aspects are mentioned. For a court trial, readers will at least want to know who is involved and what the charges and the sentence are. For a natural disaster, they will ask for the disaster type, the victims and other damages. Will a co-occurrence or frequency-based sentence extraction summariser automatically provide the requ...

Socially Driven News Recommendation

The question of how to recommend and manage information available in the Internet is an active area of research, namely concerning news recommendations. The methods used to produce news rankings by the most popular recommender systems are not public and it is unclear if they reflect the real importance assigned by the readers. Also, the latency period between the publication of a news item and ...

A Personalized News Recommendation using User Location and News Contents

Personalized news article recommendation provides interesting articles to a specific user based on the preference of the user. With increasing use of hand-held devices, the interests of users are not influenced only by news contents, but also by their location. Therefore, in this paper, we propose a novel topic model to incorporate user location into a user preference for the location-based per...

Effective hierarchical vector-based news representation for personalized recommendation

With amount of information on the web, users often require functionality able to filter the content according to their preferences. To solve the problem of overwhelmed users we propose a content-based recommender. Our method for the personalized recommendation is dedicated to the domain of news on the Web. We propose an effective representation of news and a user model which are used to recomme...