Space interest point recommendation method of considering both diversity and personalization

Abstract

The invention provides a space interest point recommendation method of considering both diversity and personalization, and relates to the technical field of space interest point recommendation. The method comprises: constructing a geography-society relationship model; calculating relevance which is of place pairs in the model and on locations and society connection; constructing a relevance matrixW; dividing a user society relationship network graph G constructed in the model; calculating a loss function in dividing; selecting an eigenvector enabling the loss function to be smallest, and dividing vertices in the graph G to obtain k interest point sets with the diversity; and selecting an interest point, which best fits user preference, from each of the k interest point sets to form an interest point recommendation list fusing the diversity and the personalization. According to the space interest point recommendation method of considering both the diversity and the personalization provided by the invention, the geography-society relationship model of interest points, a spectral clustering algorithm and a matrix decomposition algorithm are fused, and the diversity is also consideredwhile interest points recommended for a user are enabled to have a higher accuracy rate.

Description

technical field [0001] The invention relates to the technical field of spatial interest point recommendation, in particular to a spatial interest point recommendation method that takes diversity and individuality into account. Background technique [0002] With the development of the mobile Internet and the popularization and application of smart mobile devices, the data of points of interest (that is, places of interest to users, such as restaurants, movie theaters, scenic spots, etc.) has become more and more abundant, and it has also attracted the attention of a large number of data mining researchers. A good point-of-interest recommendation method can not only discover new places of interest for users and broaden their horizons, but also help merchants develop various location-based services and increase revenue. At present, most point-of-interest recommendation algorithms are mainly researched from three aspects, namely, the geographical characteristics of points of int...

AI Technical Summary

Problems solved by technology

However, most of the above-mentioned point-of-interest recommendation algorithms focus on the accuracy of the recommendation results, while ignoring the diversity of the recommendation results. As a result, the recommendation results are usually relatively similar, which cannot effectively expand the user's field of vision.

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