Andere Kunden interessierten sich auch für
![Hybrid Recommender System For Spatiotemporal Data]( "Hybrid Recommender System For Spatiotemporal Data")
Hybrid Recommender System For Spatiotemporal Data26,99 €![Web Usage Pattern Discovery using Data mining and Statistical analysis]( "Web Usage Pattern Discovery using Data mining and Statistical analysis")
Web Usage Pattern Discovery using Data mining and Statistical analysis32,99 €![Efficient Techniques for Patterns Analysis using Web Usage Mining]( "Efficient Techniques for Patterns Analysis using Web Usage Mining")
Efficient Techniques for Patterns Analysis using Web Usage Mining32,99 €![Dynamic Recommender System]( "Dynamic Recommender System")
Dynamic Recommender System36,99 €![Computer Based Recommender System]( "Computer Based Recommender System")
Computer Based Recommender System36,99 €![Privacy Risks for Memory-based CF Recommender System]( "Privacy Risks for Memory-based CF Recommender System")
Privacy Risks for Memory-based CF Recommender System36,99 €![Design And Development Of Hybrid Recommender System For Tourism]( "Design And Development Of Hybrid Recommender System For Tourism")
Design And Development Of Hybrid Recommender System For Tourism32,99 €
Recommender System is a wide area that has many sub fields that require a deep understanding and great research efforts. In particular the main aspects are: information inputs that are used by the algorithm that impacts the recommendations, algorithms that are hidden background and run the recommendation engine to predict the user's preferences, evaluation metrics that defines the satisfaction of the user and the quality of the recommendations.The sole dependency on user profile based on navigation history alone cannot promise the quality of recommendations in terms of accuracy and diversity because of lack of semantics in the processing. The time parameter in recommender systems should be considered on top of conceptual semantics as it has a great influence on item's popularity and user's preferences. The traditional evaluating metrics could not able to deal cold-start problem, that occurs with new users and new or less popular items in the web domain, because of the traditionalfiltering methods that mix up all users and items with same intent.