Recommendation System 2021: Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications |
Submission link | https://easychair.org/conferences/?conf=recommendationsystem |
Call for Book Chapters 2021: Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications.
Recommendation System is an intelligent computer based system which serves as a guide and suggests, as per the preferences of the person. It uses state of the art technologies like Big Data, Machine Learning and Artificial Intelligence etc., and benefits both the consumer and the merchant. Recommendation System is becoming very popular as it serves as a guide, for the activity that a person or a group plans to perform in the best possible manner, given the constraints imposed by the user(s). The recommendations given are to inspire its users to buy different products, which is a music creation initiative that includes specialists in a number of fields, including Artificial Intelligence, Human Computer Interaction, Data Mining, Analytics, Adaptive User Interfaces, Decision Support Systems etc. From e-commerce websites to tour operator websites recommendation systems has great importance to attract the customers. Knowledge of recommendation system is very low and this lack of awareness leads to high demand of the experts in the industry.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal, conference, book chapter etc. Similarity index should be less than 15% with less than 2% from a single source.
Submission Link: https://easychair.org/conferences/?conf=recommendationsystem
Deadline
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Deadline for paper submission: March 28, 2021
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First round notification for revision: April 30, 2021
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Final Paper submission: May 25, 2021
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Final Acceptance: 15 June, 2021
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Publication date: Last quarter of 2021
List of Topics
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AI in problem solving
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Data science in multidisciplinary research
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Technology enabled recommendation system
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AI algorithms for recommendation system
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Knowledge-based recommender Systems
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Ensemble-Based and Hybrid Recommender Systems
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Context-aware, mobile, location based recommender systems
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Evaluation metrics for performance comparison
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Recommendation algorithms
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Personalization
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Preference elicitation
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Group recommender systems
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Movie recommendations
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Music recommendations
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News recommendations
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Tourism recommendations
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Machine learning for recommendation
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Deep learning for recommendation
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Conformal recommender systems
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Transfer learning in recommender systems
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Trust and reputation in recommendation system
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Security and privacy in recommendation system
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User modelling
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Case studies
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Different applications of recommendation system
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Future research direction in recommender system
Editors
Dr. Abhishek Majumder, Tripura University, Tripura, India
Mr. Joy Lal Sarkar, Tripura University, Tripura, India
Dr. Arindam Majumder, NIT Agartala, India
Publication
Accepted chapters will be published in the book titled, "Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications" by Bentham Science.
Contact
All questions about submissions should be emailed to abhim@tripurauniv.in, joylalsarkar@gmail.com and arindam2012@gmail.com