Abstract
Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results.
Highlights
With the rapid development of spatial information technology, spatial information such as smart sign-in, mobile services, and GPS has become one of the research hotspots in recent years
The number of single sign-in data is large, it is usually sparse in time and space [10], which reduces the reliability of user similarity calculation and the quality of neighboring search, which is not good. e effect needs to be improved
E existing location recommendation algorithm considers the geographical location factor less when calculating the user similarity. In view of this problem, this paper proposes a fusion algorithm that integrates the geographic location preference and multisimilarity measure to improve the proximity of users by calculating the user similarity
Summary
With the rapid development of spatial information technology, spatial information such as smart sign-in, mobile services, and GPS has become one of the research hotspots in recent years. The number of single sign-in data is large, it is usually sparse in time and space [10], which reduces the reliability of user similarity calculation and the quality of neighboring search, which is not good. For the traditional recommendation problem, in order to improve the search quality of neighboring users, researchers have improved the similarity calculation method. E sexual measurement method, considering the relative error of user scores, improves the search quality of neighboring users, but this method does not consider the influence of time and geographical location on the recommendation results. In location recommendation, existing research methods use friend relationships and check-in time information to improve the quality of neighborhood search [13]. E existing location recommendation algorithm considers the geographical location factor less when calculating the user similarity.
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