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Tourism recommendation systems play a vital role in providing useful travel information to tourists. However, existing systems rarely aim at recommending tangible itineraries for tourists within a specific POI due to their lack of onsite travel behavioral data and related route mining algorithms. To this end, a novel travel route recommendation system is proposed, which collects tourist onsite travel behavior data automatically regarding a specific POI based on smart phone and IoT technology. Then, the proposed system preprocesses the behavior data to transform raw behavior sequences into Tourist-Behavior pattern sequences. Subsequently, the system discovers frequent travel routes from the generated pattern sequences by using an original route mining algorithm, named Tourist-Behavior PrefixSpan. Finally, a route-recommending method is designed to search and rank tangible travel routes according to the querying tourist's profile and constraint. The experimental results demonstrate that the proposed system is efficient and effective in recommending POI-oriented tangible travel routes considering tourists' route constraints and personal profile while ensuring that the suggested routes have considerable route values.

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KANO model classification is helpful for us to recognize customer needs and to improve their satisfaction. The traditional method uses standard questionnaires to conduct surveys, classifies product attributes according to the survey results. However with the increase of product complexity and the speed of product iteration, the method of survey is more and more unable to meet our analysis needs; coupled with the increasing number of customers who do not want to give feedback for questionnaires, low responds ratio rate leads poor feedback quality which affects the reliability of the research results. Although many studies are about KANO model classification, few of them focus on how to improve responds ratio rate. This article creates a new method for KANO model classification. By collecting customer reviews and rating score, we build up regression model between the score and the degree to which product attributes meet user needs according to their text expression. Based on the curve shape of the model coefficients and the value of the coefficient we can identify which KANO classification will a product attribute belongs to. The experiment study for gaming notebook has proved that this method is efficient and can be widely used in other products. We call this method as KKMA (Kano, K-means, MDS, Ad boost).

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