VOODOO SESSION ANNOUNCENT
Dr. Huzefa Rangwala (George Mason University, USA) Huzefa Rangwala is an Associate Professor at the department of Computer Science & Engineering, George Mason University. He was a Visiting Faculty at Department of Computer Science, Virginia Tech in 2015-2016.
He received his Ph.D. in Computer Science from the University of Minnesota in the year 2008. His research interests include machine learning, learning analytics, bioinformatics and high performance computing.
He is the recipient of the NSF Early Faculty Career Award in 2013, the 2014 GMU Teaching Excellence Award and the 2014 Mason Emerging Researcher Creator and Scholar Award. His research is funded by NSF, NIH, NRL, DARPA, USDA and nVidia Corporation.
Google Scholar profile: Huzefa Rangwala @ Google Scholar
Contact him at firstname.lastname@example.org
Recommender Systems in Educational Data Mining: Incorporating Semantics
The application of big data approaches, specifically methods inspired from recommender system domain to predict student performance is largely a new area of research.
The types of solutions available depend largely on the type of available data, and problem definition.
For instance, for the purposes of degree planning, one task is to predict grades for a student in a class in the future (or in the next term).
I will present an overview of opportunities for integrating semantic information with recommender systems to solve pressing problems in higher education.
13.00 - 15:00 - Session 1:
13.00 - 13.15 - Welcome Message - Opening remarks;
13.15 - 14.00 - Dr. Huzefa Rangwala (Keynote);
- 14.00 - 14.30 - User Profiling based Deep Neural Network for Temporal News Recommendation;
- 14.30 - 15.00 - Entity Recommendation Via Integrating Multiple Types of Implicit Feedback In Heterogeneous Information Network;
15.00 - 15.15 - Coffe break
15.15 - 18:00 - Session 2:
- 15.15 - 15.35 - Word Semantics based 3D Convolutional Neural Networks for News Recommendation;
- 15.35 - 16.05 - Sequential Heterogeneous Attribute Embedding for Item Recommendation;
- 16.05 - 16.35 - Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries;
- 16.35 - 16.55 - Leveraging Moderate User Data for News Recommendation;
17.00 - 17.45 - Open Discussion
17.45 - 18.00 - Closing remarks
Call for Papers:
A recommender system is designed to suggest items that are expected to interest a user. In order to filter the items and produce the recommendation, Data Mining techniques are largely employed. Among the most popular recommendation approaches in the literature and in real-world applications (e.g., e-commerce websites) are the so-called content-based recommender systems. Content-based recommender systems suggest to users items that are similar to those they previously evaluated . The early systems used relatively simple retrieval models, such as the Vector Space Model, with the basic TF-IDF weighting.
Simple (word-based) interest descriptions may fall short both because of semantic ambiguity and because they lack of generality. Recently, content-based recommender systems evolved and started employing external knowledge sources (e.g., ontologies) to improve accuracy and scope of recommendations , .
More recent approaches have been based on deep learning . Other approaches, such as , have employed word embeddings in the recommendation process. Among the best known and high-performance implementations following these lines of research we mention Google’s word2vec.
Moreover, semantic technologies will soon find a connection with cognitive computing , cooperating in the definition of the so-called cognitive recommender systems. Given the rapid advances of Semantic Technologies, there is still a large number of options for recommender systems to take advantage of semantics.
Our workshop will solicit contributions in all topics related to employing Semantic Technologies in Recommender Systems, focused (but not limited) to the following list:
- Novel approaches to user profiling in recommender systems that model behavior with semantic technologies;
- Cognitive recommender systems;
- Content-based recommendation algorithms that employ novel uses of semantic technologies;
- Recommendation explanation using semantic technologies;
- Generation of novel, diverse, and serendipitous recommendations using semantic technologies;
- Hybrid recommender systems that combine semantic technologies with other recommendation techniques (e.g, collaborative);
- Group-based approaches that use semantic technologies to describe the group preferences or to generate recommendations.
 Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro, “Semantics-aware content-based recommender systems,” in Recommender Systems Handbook, pp. 119–159. Springer US, 2015.
 Michel Capelle, Frederik Hogenboom, Alexander Hogenboom, and Flavius Frasincar, “Semantic news recommendation using wordnet and bing similarities,” in Proceedings of the 28th Annual ACM Symposium on Applied Computing, New York, NY, USA, 2013, pp. 296–302, ACM.
 Tomas Mikolov, Quoc V. Le, and Ilya Sutskever, “Exploiting similarities among languages for machine translation,” CoRR, vol. abs/1309.4168, 2013.
 Cataldo Musto, Giovanni Semeraro, Marco de Gemmis, and Pasquale Lops, “Learning word embeddings from wikipedia for content-based recommender systems,” in Advances in Information Retrieval - 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings. 2016, vol. 9626 of Lecture Notes in Computer Science, pp. 729–734, Springer.
 Dharmendra S. Modha, Rajagopal Ananthanarayanan, Steven K. Esser, Anthony Ndirango, Anthony J. Sherbondy, and Raghavendra Singh, “Cognitive computing,” Commun. ACM, vol. 54, no. 8, pp. 62–71, Aug. 2011.
Types of contributions:
We will consider three different submission types, all in the IEEE 2-column format: regular (8 pages), short (4 pages) and extended abstracts (2 pages).
Research and position papers (regular or short) should be clearly placed with respect to the state of the art and state the contribution of the proposal in the domain of application, even if presenting preliminary results. In particular, research papers should describe the methodology in detail, experiments should be repeatable, and a comparison with the existing approaches in the literature should be made where possible. Position papers should introduce novel point of views in the workshop topics or summarize the experience of a researcher or a group in the field.
Insights and results papers (short) should provide a presentation of ideas and insights, along with the results that validate these ideas, to have quick and inspiring exchanges among the workshop attendants. The “insights and results” papers will be presented in a novel and dedicated “Voodoo Session” (inspired by the location of the workshop), aimed at stimulating these exchanges.
Practice and experience reports (short) should present in detail the real-world scenarios in which Semantic Technologies are employed for recommendation purposes.
Demo proposals (extended abstract) should present the details of a prototype or complete application that employs Semantic Technologies in Recommender Systems. The systems will be demonstrated to the workshop attendees.
Accepted papers will be included in the IEEE ICDM 2017 Workshops Proceedings volume published by
IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library.
The workshop proceedings will be in a CD separated from the CD of the main conference.
The CD is produced by IEEE Conference Publishing Services (CPS).
Authors of selected papers will be invited to submit an extended version in a journal special issue.
All papers must be formatted according to the IEEE Computer Society proceedings manuscript style, following IEEE ICDM 2017 submission guidelines available at http://icdm2017.bigke.org/.
Papers should be submitted in PDF format, electronically, using the CyberChair submission system, available at: ICDM-SERecSys@Cyberchair
For general enquires regarding the workshop, send an email to: email@example.com