Cross-domain Recommendations for Personalized Semantic Services

Abstract

An increasing amount of work has been published in various areas related to the Recommender System. Among them, cross-domain recommendation is an emerging research topic and in this field, it is important to investigate how to manage personalization and how to consider customer's contextual features to keep more user satisfaction and accuracy. This paper tends to provide cross-domain recommendations for personalized semantic services using Taxonomic CCBR, directed acyclic graph by FordFulkerson algorithm and TOPSIS method. Taxonomic CCBR helps the system get the accurate problem by engaging a user in a series of questions and answers from the user's partial definition of the problem. Semantic concepts between different domains are considered by using weighted directed acyclic graph to find meaningful solutions. Then TOPSIS method is used to get the results more precisely considering contextual features such as season, place, etc. which have not been addressed in the current cross-domain recommender systems.