[Objective] This study tries to provide personalized recommendations for tourists, aiming to improve the low efficiency of user decision-making due to information overload.[Methods] We proposed a new SPT (user Similarity, Popular spot and Time) algorithm, and used real data from Ctrip to compare its recommendation results with traditional algorithms. We also proposed a method to construct training set based on “segmented user groups” and examined its impacts on the recommendation results.[Results] The SPT algorithm yielded better results than traditional recommendation methods in precision, recall, coverage and popularity. The algorithm based on “segmented user groups” further improved the effectiveness of recommendation. The precision and recall of the proposed algorithm reached 43.75% and 61.59%.[Limitations] The algorithm could not find similar users for new users. Our new method requires further testing with more datasets.[Conclusions] The proposed method improves recommendation results of tourism attractions, as well as tourists’ decision-making and personalized services.
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