Design and Application of Multiattribute Tourist Information Recommendation Model Based on User Interest

Abstract

There exist various challenges introduced by a large number of multimedia photos and videos for personalized travel recommendation in the era of big data. In order to resolve such challenges, a context-aware personalized travel recommendation system based on data mining is proposed in this study. It is a framework that can locate and summarize travel locations based on a user-given collection of geotagged photos and build up each user’s travel history to obtain their travel preferences, so as to perform contextual multiattribute personalized queries, thereby recommending travel locations that best suit their interests. The primary objective is to lay the foundation for developing personalized travel solutions and help the transformation and upgrading of the tourism industry. Thus, this paper proposes a design and application of a multiattribute travel information recommendation model based on user interests for the contradiction between the personalized travel demand of tourists and traditional travel methods. It analyzes the designed travel transportation network and builds a prototype system for travel recommendation by mining a large number of scenic spot information datasets. In association to this, an advanced recommendation algorithm is also designed. The experimental results reveal the fact that by integrating various attributes, the comprehensive evaluation mechanism of scenic spots is capable of providing enhanced reasonable and comprehensive evaluation of scenic spots, which lays the foundation for subsequent route recommendation. Secondly, in comparison to the existing path recommendation algorithms, the recommendation algorithm proposed in this paper has the potential to meet various constraints and goals of the users and recommend routes that have better reasonableness and diversity. Also, this algorithm has low complexity in terms of running time which acts as an added advantage.