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RecSysRecommender Systems (International Conference)
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From an October 2018 international conference at Bay of Roses, Catalonia on artificial intelligence, 46 contributed papers cover logic, satisfiability, and fuzzy sets; classifiers, networks, and machine learning; data science, recommender systems, and case-based reasoning; natural language and sound processing; cognitive systems and agents; and computer vision and robotics.
Recommender Systems collect preference information for a set of users or items [1].
The special tracks program included sessions and papers on AI for big social data analysis; AI for games, serious games, and multimedia; AI in healthcare informatics; applied natural language processing; autonomous agents and robots; intelligent learning technologies; recommender systems; uncertain reasoning; case-based reasoning; and semantics, logics, information extraction, and AI.
Nine of the most impressive current examples of machine intelligence include IBM Watson, driverless cars being produced by Google and other companies, applications of deep learning such as the DeepMind programmes for Go and chess, speech recognition programs such as Alexa, Siri, and Dragon NaturallySpeaking, Google Translate, recommender systems used by Netflix, Amazon, and other companies, face recognition used by Facebook, robots produced by Boston Dynamics, and poker players developed at the University of Alberta and Carnegie Mellon University.
They note that in addition to translation, AI applications are also emerging in speech recognition, computer vision, and recommender systems, with applications ranging from medical diagnoses to customer support, and from hiring decisions to self-driving vehicles.
This is what actually required from online applications with very big data like recommender systems. A vivid example of such systems is the Amazon website that suggests many items to its users through personalized webpages [3-6].
Algorithms and methods in recommender systems. Berlin, Germany: Berlin Institute of Technology.
In this way, civic life becomes opaque and arbitrary: a lottery."To combat digital distraction, they'd throttle email on Sundays and build apps for meditation," Krumme writes."Instead of recommender systems that reveal what you most want to hear, they'd inject a set of countervailing views.
Recommender systems are utilized for personalizing information sources for users by guiding them in a personalized way to interesting items selected from myriad of available options (Lops et al., 2011).
Cao, "Social networking meets recommender systems: survey," International Journal of Social Network Mining, vol.