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NRS 2013
We are delighted to organise the first International Workshop and Challenge on News Recommender Systems at the 7th ACM Recommender System Conference (RecSys 2013) in Hong Kong. This workshop intends to bring together researchers and practitioners around the topics of designing and evaluating novel news recommender systems in order to: (1) share research on news recommendation techniques and evaluation methodologies (2) explore key challenges in the area, and (3) identify emerging topics. Additionally, a challenge will allow participants to evaluate their method by directly interacting with a real-world news recommender systems. The challenge will feature a data set designed to bootstrap a news recommender system. During the weeks before the conference, we will record how well each participant’s system performs with respect to the ratio of clicks per recommendation request. The observed performances will be outlined. We will award the best performing systems. This workshop aims at creating an interdisciplinary community with a focus on the design issues for news recommender systems and promoting the collaboration opportunities between researchers and practitioners.
The workshop will be held a sequence of talks structured as sessions. Such sessions will allow participants to present their work. We reserve one session to present the challenge’s results. Participants who took part in the challenge will have the chance to present their method during this session. Besides challenge contributions, we welcome submissions covering topics related to news recommender systems. Such topics include but are not limited to:
Topics of interest
- Recommandation techniques
- Tracking of news evolution
- Evaluation approaches
- The interplay of news and social-media data
- User interface issues
- News mining and analytics
- News reader behavioural models
- Semantic and news context analysis
- User profiling and preference elicitation
- News recommendations on mobile platforms
- Information retrieval in news collections
Demonstration at WWW 2013
Our demo paper is accepted at WWW 2013
Mobile news recommender systems help users retrieve news that is relevant in their particular context and can be presented in ways that require minimal user interaction. In spite of the availability of contextual information about mobile users, though, current mobile news applications employ rather simple strategies for news recommendation. Our multi-perspective approach unifies temporal, locational, and preferential information to provide a more fine grained recommendation strategy.
The NTNU Smartmedia program at the Department of Computer and Information Science, Norwegian University of Science and Technology, was established in 2012 in close collaboration with the Scandinavian media industry. As the industry is addressing the abundance of news and information in general from news agencies and social sites, as well as open data from public and private institutions, it has become paramount to develop architectures and technologies for large-scale realtime data processing. The intention of Smartmedia is to look into new technologies that may help the companies and their journalists deal with the explosion of online information and present news more efficiently and attractively to readers. Central to this program are technologies like:
- Big Data architectures
- Information retrieval and recommendation
- Semantics
- Text analytics and sentiment analysis
- Mobile platforms
.