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RecSysKTL 2017 : Workshop on Intelligent Recommender Systems by Knowledge Transfer and Learning


When Aug 27, 2017 - Aug 27, 2017
Where Como, Italy
Submission Deadline Jun 22, 2017
Notification Due Jul 7, 2017
Final Version Due Jul 17, 2017
Categories    recommender sysems   artificial intelligence   data mining   machine learning

Call For Papers

Generally, we focus on the topic of “cross-domain”, where the notion of “domain” may vary from applications to applications. For example, the concept of context-aware and multi-criteria recommender systems can also be considered as an application of “cross-domain” techniques. Particularly, we are interested in how to apply knowledge transfer and learning approaches to build intelligent recommender systems.

The topics of interest include (but are not limited to):

Applications of Knowledge Transfer for Recommender Systems
Cross-domain recommendation
Context-aware recommendation, time-aware recommendation
Multi-criteria recommender systems
Novel applications
Methods for Knowledge Transfer in Recommender Systems
Knowledge transfer for content-based filtering
Knowledge transfer in user- and item-based collaborative filtering
Transfer learning of model-based approaches to collaborative filtering
Deep Learning methods for knowledge transfer
Challenges in Knowledge Transfer for Recommendation
Addressing user feedback heterogeneity from multiple domains (e.g. implicit vs. explicit, binary vs. ratings, etc.)
Multi-domain and multi-task knowledge representation and learning
Detecting and avoiding negative (non-useful) knowledge transfer
Ranking and selection of auxiliary sources of knowledge to transfer from
Performance and scalability of knowledge transfer approaches for recommendation
Evaluation of Recommender Systems based on Knowledge Transfer
Beyond accuracy: novelty, diversity, and serendipity of recommendations supported by the transfer of knowledge
Performance of knowledge transfer systems in cold-start scenarios
Impact of the size and quality of transferred data on target recommendations
Analysis of the amount of domain overlap on recommendation performance
Submissions Guidelines

We accept long papers (up to 8 pages) and short papers (up to 4 pages) in ACM conference format (references are counted in the page limit). Long papers are expected to present original research work which should report on substantial contributions of lasting value. Short papers may discuss the late-breaking results or exciting new work that is not yet mature, or open challenges in promising research directions. The accepted papers will be invited for presentations and the proceedings will be available at, while the authors will hold the copyrights.

All of the submissions should be submitted via EasyChair system:

We are working on a special issue, and the authors will be invited to submit the extension of their work to the special issue in a journal. More information will be released later.

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