Abstract
The Geosocial networks integrate geographical location information into traditional social networks, bridging the gap between people's real-life experiences and the virtual world. As a significant application of geosocial networks, location recommendation suggests places that individuals may find interesting, offering valuable references for their travels and greatly enhancing their lives. Consequently, the challenge of recommending relevant locations to users from a vast pool of geographic options has become a prominent topic in academic research. Collaborative filtering algorithms stand as one of the classic solutions in the field of recommendations. While they partially address the problem of information overload, they often encounter a common obstacle known as the cold start problem. To overcome this issue, this study makes two primary contributions: Firstly, it proposes the utilization of Markov chains to mitigate the cold start problem. Secondly, it introduces a hybrid recommendation model called HMGR for location-based recommendations, which effectively enhances the accuracy of suggestions. We evaluate the efficacy of the Markov chain and HMGR model through extensive experimentation. The results demonstrate that the implementation of Markov chains successfully alleviates the cold start problem, and our HMGR model significantly improves the precision of recommendations.
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Acknowledgement
This research is supported by the National Natural Science Foundation of China (No. 61962029, No. 62062045, No. 62262033), the Jiangxi Provincial Natural Science Foundation of China (No. 20202BAB212006) and the Science and Technology Research Project of Jiangxi Education Department (No. GJJ201832).
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Wen, R., Cheng, Z., Mao, W., Mei, Z., Shi, J., Cheng, X. (2023). HMGR: A Hybrid Model for Geolocation Recommendation. In: Zhang, S., Hu, B., Zhang, LJ. (eds) Big Data – BigData 2023. BigData 2023. Lecture Notes in Computer Science, vol 14203. Springer, Cham. https://doi.org/10.1007/978-3-031-44725-9_4
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