Integrating contextual information into multi-class classification to improve the context-aware recommendation.

Researchers and practitioners in various fields, including e-commerce customization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management, have realized the value of contextual information. Context-aware recommender systems assist users in finding their chosen material in a reasonable amount of time by utilizing information that describes the scenario in which the items will be consumed. For better personalized user recommendation, recommender systems leverage the contextual information in their process of recommendation called context-aware recommendation. Classification is used for context-prediction which represents the prediction of future context based on recorded previous context. The context prediction algorithm's goal is to recognize typical behavior patterns that have been seen in the past and then offer the most likely continuation of a presently observed collection of context components based on this knowledge. In this article we study the correlation between the multi-class classification and the context-aware recommendation.With this correlation we conclude that the linkage between contextual information and classification enhance and improve the recommendation results. [ABSTRACT FROM AUTHOR]