An Algorithm for Personalized Tourism Recommendation Based on Time Series
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For travelling, a tourist’s choice of one destination spot is inevitably influenced by both their previous favourite and successive plan. In view of this, this paper proposes a recommendation algorithm based on time series incorporating three clustering algorithms, i.e., K-Means, MCA and Build Classification. The idea of this algorithm is to find the authoritative users of a spot, cluster their evaluated resources, find the previous and successive relevance of this spot according to the evaluation time, and then recommend the successive spot of the current focus to the user in sequence.
Algorithm, Recommendation, Time Series, Spot