Special Issue "Advances in Recommender Systems"
A special issue of Informatics (ISSN 2227-9709).
Deadline for manuscript submissions: closed (31 March 2018) | Viewed by 31814
Special Issue Editor
Interests: recommender systems; machine learning; artificial intelligence
Special Issue Information
Dear Colleagues,
The importance of recommender systems (RS) has grown exponentially with the advent of social networks and the Internet of things. RS are the main solution to the information overload problem. RS acts as filters between users and items allowing the passage of relevant items to the user and blocking the irrelevant ones. Nowadays, RS make use of different sources of information for providing users with predictions and recommendations. They try to balance factors like accuracy, novelty, diversity, serendipity, trust and stability in recommendations.
RS can be classified according to the type of information that they use to compute recommendations. Most popular classifications divides RS into collaborative filtering (CF) and content-based filtering (CBF). CF predicts unknown users’ ratings using clusters of users and items built with the ratings of the users to the items. CBF compares users’ preferences with content information about the items (such as description, tags or location) in order to compute recommendations.
We solicit original submissions that improve RS on any of the following topics:
- collaborative filtering: Similarity metrics, quality measures, matrix factorization, cold start
- content based filtering: Topic modeling, folksonomies, geo-based recommendations
- social media data: Recommendations to group of users, followers, time-based recommendations
- deep learning in recommender systems
Guest Editor
Manuscript Submission Information
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Keywords
- Recommender systems
- Collaborative filtering
- Content based filtering
- Matrix factorization
- Deep learning