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..
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
User Name : Jackson
Posted 02-03-2017 on 15:35:28 AEDT