POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
Source abstract
Abstract
The utilization of location-based social networks to provide point-of-interest (POI) recommendation services has been the subject of extensive research in recent years. Various factors that can enhance the precision of POI recommendations were examined in previous studies. However, the factors of a user, including the location and time, were not considered. In this paper, we proposed a POI recommendation scheme in which user activity patterns and the similarity of categories are considered. The proposed scheme is used to organize users based on the activity level and to take into account the characteristics of both the user and location. Furthermore, it provides personalized recommendations by considering the category similarity, time, and location data that were collected from users. We evaluated the performance of the proposed scheme and compared it with that of a currently used scheme. The proposed scheme exhibits precision that is approximately 16% greater than that of the existing scheme.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
Department of Big Data, Chungbuk National University, Cheongju 28644, Republic of Korea
School of Computer Science and Technology, Xidian University, Zunyi 563000, China
Department of Artificial Intelligence Convergence, Wonkwang University, Iksan 54538, Republic of Korea
Funding
National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) · Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT)
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