Large-Scale Recommender Systems: Research and Best Practice As we enter the era of Big Data, the modern Recommender System faces greatly increased data volume and complexities. Previous computational models and experience on small data may not hold today, thus, how to build an efficient and robust system has become an important issue for many practitioners. Meanwhile, there is an increasing gap between academia research of recommendation systems focusing on complex models, and industry practice focusing on solving problems at large scale using relatively simple techniques. Chances favor connected minds. The motivation of this workshop is to bring together researchers and practitioners working on large-scale recommender system in order to: (1) share experience, techniques and methodologies used to develop effective large-scale recommender, from architecture, algorithm, programming model, to evaluation (2) identify key challenges and promising trends in the area, and (3) identify collaboration opportunities among participants. We invite industrial level recommendation system practitioners to submit extended abstracts (1-4 pages), or slides (~10pages). We also invite recommendation systems researchers to submit extended abstract (1-4 pages) on their new research related to system aspect of recommendation with Big Data. Our topics of interests include, but are not limited to: Systems of Large-scale RS: Architecture Programming Model Distributed systems Real-time recommendation Online learning for recommendation Scalability and Robustness Data & Algorithms in Large-scale RS: Big data processing in offline/near-line/online modules Streaming data for recommendation Data platforms for recommendation Large, unstructured and social data for recommendation Heterogeneous data fusion Sampling techniques Parallel algorithms Incremental algorithms Algorithm validation and correctness checking Application & Evaluation of Large-scale RS: Emerging applications Explanations in Large-scale RS Anti-attack of Large-scale RS Large data and privacy issue Evaluation methodology Large user studies Measurement platforms Visualization
Posted on: Sat, 03 Aug 2013 09:37:48 +0000
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