Recommender systems are acknowledged as an essential instrument to support users in finding relevant information in an overloaded information space. While they have been proven successful in e.g., e-commerce applications, they can also support organizations in better identifying competences, help engage users in a continuous and dynamic knowledge exchange, and customize dissemination of knowledge as much as possible. Moreover, the advent of the big data era has posed the need for high scalability and real-time processing of frequent data updates, and thus, has brought new challenges for the recommender systems’ research community. As in 2016 (http://socialcomputing.know-center.tugraz.at/rs-bda/), the objective of this workshop is to bring together researchers and practitioners involved in developing, testing, and maintaining (social) recommender systems, especially in the light of big data. The workshop focuses on all aspects of recommender systems and big data analytics and it will provide a forum for discussing current practices and recent research results. RS-BDA'17 is co-located with i-KNOW 2017 (http://i-know.tugraz.at/).
The program of the workshop will consist of an invited tutorial talk by Anthony Jameson and several accepted paper talks.
Using Chusapedia to Design Applications That Support Human ChoicePresenter
Anthony Jameson is a Principal Researcher at DFKI, the German Research Center for Artificial Intelligence and founder of the new startup Chusable AG, which specializes in software for helping people to make better choices. Since the late 1970s, he has done research and practical work on various types of interactive intelligent system, combining knowledge of psychology, artificial intelligence, and human-computer interaction. From 2009-2016 he was founding coeditor-in-chief of the ACM Transactions on Interactive Intelligent Systems. Work especially related to the Chusapedia system presented at i-KNOW 2017 includes (a) the theoretical integration of research on choice and choice support expounded in the 2014 book Choice Architecture for Human-Computer Interaction and in the handbook chapter Human Decision Making and Recommender Systems; and (b) work on systems that integrate knowledge using semantic technology: participation in Project Halo; and the leadership of 3cixty, which won the 2015 Semantic Web Challenge.Abstract
This tutorial offers guided hands-on experience with the web-based system presented in the keynote talk titled “Chusapedia: Knowledge-Based Design of Applications That Support Human Choice”; please see the abstract of that talk for a brief introduction to Chusapedia.
In the tutorial, hands-on use of Chusapedia will alternate with explanations and illustrations of the various parts of its knowledge base. A typical example of an interactive application that supports a particular type of choice will be used as a running example. Participants will see how the key features of the example application can be characterized in terms of tactics for supporting particular steps in the processes by which people make choices. Participants will then be able to access other knowledge available in Chusapedia to derive additional ideas about how the example application could be redesigned to support choice more effectively. They will also learn how to use Chusapedia in a domain of interest to them and how to contribute knowledge and solutions to the system so as to make it more useful for the design of applications in their chosen domain.
No specialized knowledge is required for participation. Each participant should bring a tablet or laptop with which they can conveniently navigate and edit pages in a Wikipedia-style website. Information about the availability of Chusapedia can be found on http://chusable.com.
The main topic of this workshop is the broad research area of recommender systems and how it is connected with big data analytics. Thus, it is our main intention to bring together researchers and practitioners in these areas to discuss novel trends in analyzing big data for recommender systems. This workshop theme should be of great interest for i-KNOW 2017 attendees since both recommender systems and big data analytics are important research instruments at the intersection of the disciplines of knowledge discovery, Web & data science as well as social computing.
Overall topics to be adressed by this workshop include but are not limited to:
Within these topics, we encourage demo papers (max. 2 pages), short research papers (max. 4 pages) and long research papers (max. 8 pages), both in ACM double-column conference paper style. All submitted papers must:
All papers will be peer-reviewed and must not be under review in any other conference, workshop or journal. Accepted full papers will be published in the ACM Digital Library and papers accepted as short/demo will be optionally published in special online workshop proceedings.