JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (6): 54-61.doi: 10.6040/j.issn.1672-3961.0.2016.311

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Collaborative recommendation for scenic spots based on multi-aspect ratings

WANG Zhiqiang1, WEN Yimin1,2*, LI Fang1,2   

  1. 1. School of Computer Information and Security, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China;
    2. Guangxi Key Laboratory of Trusted Software, Guilin 541004, Guangxi, China
  • Received:2016-07-22 Online:2016-12-20 Published:2016-07-22

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Abstract: The simplex overall ratings are used to compute the similarities between users and items in the model of traditional collaborative filtering recommendation, but it can't correctly depict the users' true preferences. In order to solve this problem, a collaborative scenic spots recommendation algorithm based on multi-aspect ratings was proposed, which integrated the ratings of the scenery, interesting and cost performance of spots to compute the similarities to predict the overall ratings of an active user for a target spot. Experimental results showed that, after introducing the information of multi-aspect ratings, the proposed method improved the accuracy of prediction score, coverage and F-measure and reduced the predicting error of root-mean-square and mean-absolute.

Key words: rating prediction, similarity metrics, scenic spots recommendation, multi-aspect ratings, collaborative recommendation

CLC Number: 

  • TP181
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