Tourist destination recommendation method based on user emotion and time dynamic

Abstract

The invention discloses a tourist destination recommendation method based on user emotion and time dynamic. The method includes the following steps: firstly, obtaining a quantitative value of emotional inclination of a user by using opinion mining techniques, integrating emotion degree into a matrix decomposition model, and expressing user preferences and tourist destination popularity with the change of time by using a time dynamic mechanism; and then constructing a synthetic recommendation model by combining two elements of user emotion and time influence with an SVD++ method. The user preferences are automatically learned by analyzing explicit or implicit feedback, and so features of tourism leisure resources can match user needs. Compared with a plurality of famous recommendation method, the method of the invention is improved in recommendation accuracy and quality, and a series of experimental evaluations are carried out on publicly available datasets to show that a provided recommendation system is better than a conventional recommendation system.

Description

technical field [0001] The invention relates to the field of travel recommendation, in particular to a travel destination recommendation system. Background technique [0002] The amount of information available on the Internet and the number of users has increased tremendously over the past decade, and this information can be especially valuable for users planning to visit unknown tourist destinations and having to make travel plans. However, the list of possibilities that search engines provide for travel destination information is overwhelming. Users need to spend a lot of time and energy evaluating the collected information, and spend even more time extracting useful information. [0003] Recommender systems can help users cope with information overload and provide them with personalized recommendations, content, and services. In the field of tourism, the tourism recommendation system automatically learns the preferences of users by analyzing their explicit or implicit ...

AI Technical Summary

Problems solved by technology

In fact, the recommendation of tourist destinations is more complex than other tourist destinations, because the number of user reviews on tourist destinations is small and the data is more sparse, so a comprehensive profile is not easy to build

Moreover, the recommendation of tourist destinations is easily affected by factors such as seasons, traffic, and cost, so it is difficult to construct a recommendation model for tourist destinations that integrates many valuable factors.

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