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Title:
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A TRAVEL SEQUENCE RECOMMENDATION APPROACH BASED ON MARKOV MODEL |
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Author(s):
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Dalu Guo, Richong Zhang, Xudong Liu, Hailong Sun |
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ISBN:
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978-989-8533-09-8 |
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Editors:
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Bebo White and Pedro Isaías |
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Year:
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2012 |
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Edition:
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Single |
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Keywords:
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Recommender System, Visiting Sequence, Affinity Propagation |
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Type:
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Full Paper |
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First Page:
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266 |
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Last Page:
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272 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Existing recommender systems have been successfully helping travelers to find their preferred places of interest from user shared tourism related contents, such as photos, travelogues and etc. However, the visiting sequence and time cost of these recommended spots are seldom generated by these systems. This information is rather helpful for tourists to make trip decisions. In this paper, we propose a visiting sequence recommendation approach to assist potential travelers making visiting plans. In particular, we construct travelers visiting behaviors from user uploaded photos on the photo sharing platforms. Then, we present a visiting sequence discovering model by utilizing Affinity Propagation and Markov model. At the end, we conduct experiments on the collected user traveling patterns from Flickr.com to compare the performance of our model with other commonly-used approaches. The experimental results confirm the effectiveness of our proposed algorithm. |
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