RecSys - ESWC 2014 Challenges WIKI
abernard102@gmail.com 2013-12-03
Summary:
"MOTIVATION AND OBJECTIVES ... People generally need more and more advanced tools that go beyond those implementing the canonical search paradigm for seeking relevant information. A new search paradigm is emerging, where the user perspective is completely reversed: from finding to being found. Recommender systems may help to support this new perspective, because they have the effect of pushing relevant objects, selected from a large space of possible options, to potentially interested users. To achieve this result, recommendation techniques generally rely on data referring to three kinds of objects: users, items and their relations. Recent developments in the Semantic Web community offer novel strategies to represent data about users, items and their relations that might improve the current state of the art of recommender systems, in order to move towards a new generation of recommender systems which fully understand the items they deal with. More and more semantic data are published following the Linked Data principles, that enable to set up links between objects in different data sources, by connecting information in a single global data space: the Web of Data. Today, the Web of Data includes different types of knowledge represented in a homogeneous form: sedimentary one (encyclopedic, cultural, linguistic, common-sense) and real-time one (news, data streams, ...). These data might be useful to interlink diverse information about users, items, and their relations and implement reasoning mechanisms that can support and improve the recommendation process. The primary goal of this challenge is twofold. On the one hand, we want to create a link between the Semantic Web and the Recommender Systems communities. On the other hand, we aim to show how Linked Open Data and semantic technologies can boost the creation of a new breed of knowledge-enabled and content-based recommender systems ... TARGET AUDIENCE ... The target audience is all of those communities, both academic and industrial, which are interested in personalized information access with a particular emphasis on Linked Open Data. During the last ACM RecSys conference more than 60% of participants were from industry. This is for sure a witness of the actual interest of recommender systems for industrial applications ready to be released in the market ..."