An Associative Journey Scheduling Method based on Public Preference and Social Influence

Abstract

The purposed in this thesis is to develop a novel associative journey scheduling method which employs public preference and social influence to classify user preference and uses point of interest (POI) relationship to extend preference list for journey scheduling Unlike traditional content-based or collaborative filtering recommendation approaches we collected large scale information from heterogeneous data sources to construct the public preference model for user’s feature-based preference orientation classification Moreover the social influence vector of target user is constructed to analyze social influence of preference between users in it According to the different online social networks corresponding types of interaction are adopted to estimate the degree of social influence between users In addition we use a large number of articles about specific POI to analyze association degree between POIs with public preferences similarity and distance and construct POI related graph The purpose method deals with the recommended list that contains few items There two main advantages of the proposed method: 1 Any type of recommendation system can be applied in the proposed method；2 It can find out some POIs not in recommend list In our experiment the information sources (includes: blogs news and online social networks) construct public preference model Moreover Facebook the most famous social media is the platform selected for social relationship analysis The experimental result shows our approach innovation and practicable

Date of Award	2015 Aug 27
Original language	English
Supervisor	Yau-Hwang Kuo (Supervisor)