International Workshop on Semantic Technologies meet Recommender Systems & Big Data
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 of 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, 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, ...). This 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 challenge is to investigate whether and how this large amount of wide-coverage and linked semantic knowledge can be automatically introduced into systems that perform tasks requiring human-level intelligence. Examples of such tasks include understanding a health problem in order to make a medical decision, or simply deciding which laptop to buy. Recommender systems support users exactly in those complex tasks.
The primary goal of the workshop is to showcase cutting edge research on the intersection of Semantic Technologies and Recommender Systems, by taking the best of the two worlds. This combination may provide the Semantic Web community with important real-world scenarios where its potential can be effectively exploited into systems performing complex tasks.
Topics of interest include (but are not limited to):
- Recommendation approaches using Semantic technologies
- Linked Data for Recommender Systems
- Ontology-based recommendation algorithms
- Merging and ranking recommendations
- Social recommender systems
- Reasoning with Big Data
- Data Acquisition
- Discovery of relevant Linked Data sources for recommendation algorithms
- Tracking provenance, evaluating reliability, quality and trustworthiness of Linked Data
- Linking, aggregating, intertwining and mining Linked Data for recommender systems
- Integrity and privacy issues
- New Reference Architectures for Recommender Systems
- Linked Data in new Recommender Systems architectures
- Efficiency, performance and scalability issues
- Distributed architectures
- Innovative applications
- Semantic technologies for Cross-lingual and cross-domain recommender systems
- Mining user data streams
- Semantic technologies for improving transparency and explanations
- Evaluation methodologies and approaches
- Big datasets for the evaluation
- Evaluation methodologies for real time personalization in big datasets
- Semantic technologies for improving novelty, diversity and serendipity
- Link Prediction in Multi-relational Graphs using Additive Models. Xueyan Jiang, Volker Tresp, Yi Huang and Maximilian Nickel (PDF)
- Driver Recommendations of POIs using a Semantic Content-based Approach. Rahul Parundekar and Kentaro Oguchi (PDF)
- Semantic Network-driven News Recommender Systems: a Celebrity Gossip Use Case. Marco Fossati, Claudio Giuliano and Giovanni Tummarello (PDF)
- Cinemappy: a Context-aware Mobile App for Movie Recommendations boosted by DBpedia. Vito Claudio Ostuni, Tommaso Di Noia, Roberto Mirizzi, Romito Davide and Eugenio Di Sciascio (PDF)
- Ontology-based Rules for Recommender Systems. Jeremy Debattista, Simon Scerri, Ismael Rivera and Siegfried Handschuh (PDF)
- Ontology-centric decision support. Marco Rospocher and Luciano Serafini (PDF)
- RING: A Context Ontology for Communication Channel Rule-based Recommender System. Miguel Lagares Lemos, Daniel Villanueva Vasquez, Mateusz Radzimski, Angel Lagares Lemos and Juan Miguel Gómez-Berbís (PDF)
Size does not matter if your data is in a siloAbstract: The advent of "big data" has afforded many opportunities for the discovery of interesting facts and phenomena about the world where this data was collected. Lots of excitement surrounds the systems and platforms which are used in processing big data (and indeed, which are capable of the scale needed). Some of the technical optimizations needed for scaling up processing (e.g., NoSQL, key/value databases) have resulted in "weaker" or less accessible data models, and consequently tend to emphasize the notion of "siloed" data. To mitigate this, we need stronger effort on how data is described, both in terms of operational parameters, provenance, workflows, and rich data models. Semantic Web technologies are well suited to capturing all the metainformation needed, regardless of the physical formats and storage solutions used for the actual data.
- 9:00 - 9:10
- 9:10 - 9:50
Ora LassilaSize does not matter if your data is in a silo
- 9:50 - 10:10
Xueyan Jiang, Volker Tresp, Yi Huang and Maximilian NickelLink Prediction in Multi-relational Graphs using Additive Models.
- 10:10 - 10:30
Rahul Parundekar and Kentaro OguchiDriver Recommendations of POIs using a Semantic Content-based Approach.
- 10:30 - 11:00
- Coffee Break
- 11:00 - 11:20
Marco Fossati, Claudio Giuliano and Giovanni TummarelloSemantic Network-driven News Recommender Systems: a Celebrity Gossip Use Case.
- 11:20 - 11:40
Vito Claudio Ostuni, Tommaso Di Noia, Roberto Mirizzi, Romito Davide and Eugenio Di SciascioCinemappy: a Context-aware Mobile App for Movie Recommendations boosted by DBpedia.
- 11:40 - 12:00
Jeremy Debattista, Simon Scerri, Ismael Rivera and Siegfried HandschuhOntology-based Rules for Recommender Systems.
- 12:00 - 12:20
Marco Rospocher and Luciano SerafiniOntology-centric decision support.
- 12:20 - 12:40
Miguel Lagares Lemos, Daniel Villanueva Vasquez, Mateusz Radzimski, Angel Lagares Lemos and Juan Miguel Gómez-BerbísRING: A Context Ontology for Communication Channel Rule-based Recommender System.
ProceedingsThe workshop proceedings have been published as CEUR-WS Vol-919 and are avilable at http://ceur-ws.org/Vol-919
We welcome work at all stages of development: papers can describe applied systems, empirical results or theoretically grounded positions.
Accepted papers will be published as CEUR workshop proceedings (http://ceur-ws.org).
Based on the quality of accepted papers we are planning to schedule a special issue of a top-level journal in 2013.
* Full papers (10-12 pages)
* Short papers (4-6 pages)
* Demos (2-4 pages for description)
Papers should be formatted according to the general ISWC2012 submission guidelines. Accepted format is PDF.
Please submit your paper via EasyChair at the following URL:
You need to open a personal account upon the first login, if you do not have one.
- [NEW] Submission of papers:
July 31Aug 7, 2012
- [NEW] Notification of acceptance:
August 21September 5, 2012
- Camera-ready versions: September 10, 2012
- Marco de Gemmis - University of Bari Aldo Moro, Italy
- Tommaso Di Noia - Politecnico of Bari, Italy
- Pasquale Lops - University of Bari Aldo Moro, Italy
- Thomas Lukasiewicz - University of Oxford, UK
- Giovanni Semeraro - University of Bari Aldo Moro, Italy
- Fabian Abel (L3S Research Centre - Germany)
- Claudio Bartolini (HP Labs @ Palo Alto - USA)
- Marco Brambilla (Politecnico di Milano - Italy)
- Andrea Calì (Birkbeck, University of London - UK)
- Ivan Cantador (Universidad Autónoma de Madrid - Spain)
- Pablo Castells (Universidad Autónoma de Madrid - Spain)
- Federica Cena (University of Turin - Italy)
- Bettina Fazzinga (Università della Calabria - Italy)
- Tim Furche (Oxford University Computing Laboratories - UK)
- Nicola Henze (Leibniz Universitä Hannover - Germany)
- Dominikus Heckmann (DFKI - Germany)
- Leo Iaquinta (Univ.di Milano Bicocca - Italy)
- Roberto Mirizzi (HP Labs @ Palo Alto - USA)
- Ahsan Morshed (CSIRO - Australia)
- Enrico Motta (Open University in Milton Keynes - UK)
- Cataldo Musto (Università di Bari "Aldo Moro" - Italy)
- Fedelucio Narducci (Univ.di Milano Bicocca - Italy)
- Vito Claudio Ostuni (Politecnico of Bari - Italy)
- Alexandre Passant (seevl.net - Ireland)
- Gerardo I. Simari (University of Oxford - UK)
- Markus Zanker (Alpen-Adria-Universitä Klagenfurt - Austria)
- e-mail: firstname.lastname@example.org
- Web page: http://sisinflab.poliba.it/sersy2012/