Recommender Systems based on Rich Item Descriptions

  in Special Issue   Posted on August 16, 2017

Information for the Special Issue

Submission Deadline: Fri 15 Sep 2017
Journal Impact Factor : 3.625
Journal Name : User Modelling and User-Adapted Interaction
Journal Publisher:
Website for the Special Issue: http://www.springer.com/cda/content/document/cda_downloaddocument/Flyer+UMUAI+SI+RecSys+Rich+Item+Description+2017.pdf?SGWID=0-0-45-1611770-p35622709
Journal & Submission Website: http://www.springer.com/computer/hci/journal/11257

Special Issue Call for Papers:

Utilizing side information about items for user modeling and recommending including
− structured sources, e.g., DBpedia, Linked Open Data, BabelNet, Wikidata
− textual sources, e.g., Wikipedia or User-Generated Content like tags, reviews, and comments
− multimedia (“low-level”) features, e.g., videos or musical signals
Approaches that rely on a semantic (deep) understanding of items and their features based,
e.g., on formal ontologies
Applying deep learning methods to model item features
Leveraging rich item representations for more effective user modeling and recommendation
Using side information about items to increase recommendation quality in terms of novelty,
diversity, or serendipity
Using side information about items to explain recommendations to users
Leveraging side information and external sources for cross-lingual recommendations
Using side information about items for transparent user modeling compliant with the General
Data Protection Regulation
Novel applications areas for recommender systems (e.g., music or news recommendation, off-
mainstream application areas) based on item side information
User studies (e.g., on the user perception of recommendations), fiel

Other Special Issues on this journal