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• W2207364120 abstract "Modern e-commerce systems offer a multitude of products and services in global marketplaces. The modern consumer is therefore overwhelmed by millions of options, variants and choices of products and services. With the rise of global marketplaces with their huge amount of items, recommendation systems became the basis for modern ecommerce systems. The traditional approaches for implementing recommendation engines, such as content and collaborative filtering, solve the challenge of calculating a recommendation set of items for a given user. While these traditional approaches cope well with large sets of static user and item information, they lack a general approach for including highly dynamic context-information. As the e-commerce market swiftly changes to mobile computing platforms, such as smartphones and tablets, the use of context-information for generating item recommendations is of great interest. In this work, we propose a concept for a general framework for the implementation of such context-aware recommendation engines, specifically for mobile platforms. Keywords-context awareness; context aware recommendation; decision support; recommendation system;" @default.
• W2207364120 created "2016-06-24" @default.
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• W2207364120 date "2013-04-21" @default.
• W2207364120 modified "2023-09-26" @default.
• W2207364120 title "General Framework for Context-Aware Recommendation of Social Events" @default.
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