Abstract
In this paper, we try to solve the personalized travel recommendation problem by exploiting the multi-modal data available from the real world social media, and a probabilistic graph model so called Sentiment-aware Multi-modal Topic Model (SMTM) is proposed to mine the latent semantics of the multi-modal data on the online travel website. Distinguished from previous approaches, our proposed approach try to mine the topics from tourist and attraction domains separately for disclosing semantics for tourist topics and attraction themes. In addition, we analyze tourist's sentiments on attractions to further obtain the tourist's attitude over attractions and recommend the attraction with proper sentiment on the related attraction themes accordingly. Based on the proposed SMTM model, the documents in tourist domain and in attraction domain can be compared with each other after they were projected into the mutual topic space, and this latent space projection scheme can be further applied to two personalized traveling recommendations, that is, the single platform traveling recommendation and the inter-platform traveling recommendation. Evaluation results based on the real world online travel website have shown the improved performance of our method.
Highlights
Many social networks surge up with the arising of Web 2.0, leading to the tremendous online propagation of the User Generated Content (UGC), which distributes over multinetworks
Based on the proposed Sentiment-aware Multi-modal Topic Model (SMTM), we develop a travel recommendation framework, in which the documents in tourist domain and in attraction domain can be compared with each other after they were projected into the mutual topic space, and this latent space projection scheme can be further applied to two applications, that is, the single platform traveling recommendation and the inter-platform traveling recommendation
The contributions of this paper are summarized as follows: -We propose a SMTM model which takes into account of three preliminary factors in traveling recommendation problem and the advantages of SMTM model include: (1) the multi-modal data both for tourist domain and attraction domain is fully exploited for better semantics disclosing; (2) the topics in tourists domain and the themes in attractions domain are separately modeled for better disclosing relationships in corresponding semantic space; (3) the tourist sentiments on topics are studied to obtain traveler’s opinion
Summary
Many social networks surge up with the arising of Web 2.0, leading to the tremendous online propagation of the User Generated Content (UGC), which distributes over multinetworks. Exploiting and aggregating user generated data from online network rises up as a solution towards complete and timely semantic modeling to improve the performance of multimedia based applications, such as searching, annotating, recommendation and advertising. Among all these applications, intelligent travel recommendation is one of the most attractive applications for researchers because it is closely related to people’s everyday life. According to the statistics conducted by World Travel & Tourism Council, more and more travel companies provide on line services, and people, especially younger generation prefers to check the travel website for the attraction selection before they plan to visit. The expected demand of travel recommendation service will increase substantially
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