1st Workshop of Knowledge-aware and Conversational Recommender Systems

Over the last years we have been witnessing the advent of more and more precise and powerful recommendation algorithms and techniques able to effectively assess users’ tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest of the recommendation engine. Although very effective in terms of accuracy of results, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move into the direction of providing novel and diverse results as well as generate an explanation for the recommended items or support interactive and conversational recommendation processes.

This workshop will focus on all aspects related to the exploitation of external and explicit knowledge sources to feed a recommendation engine. The aim is to go beyond the traditional accuracy goal and to start a new generation of algorithms and approaches which exploit the knowledge encoded in ontological and logic-based knowledge bases, knowledge graphs as well as the semantics emerging from the analysis of semi-structured textual sources. The aim of the workshop is to bring together researchers and practitioners around the topics of designing and evaluating novel approaches for recommender systems in order to:

• share research and techniques, including new design technologies and evaluation methodologies,
• identify next key challenges in the area, and
• identify emerging topics.

KaRS 2018 is the 1st edition of the Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop. It will be held in conjunction with the ACM Conference on Recommender Systems (RecSys 2018), Vancouver, Canada, from October, 2nd to October 7th, 2018.