Abstract

With the growing demand for personalised travel experiences, the development and application of travel point-of-interest (POI) recommendation systems have become increasingly important. However, many existing systems often underperform owing to insufficient integration of social features and contextual information. To address this issue, the S-LMF algorithm is proposed, combining social features with logistic matrix factorisation to improve recommendation accuracy. This approach simulates social influence by incorporating joint check-in similarity and friendship factors, while logistic matrix factorisation leverages check-in frequency data to refine POI recommendations. The effectiveness of social features and logistic matrix factorisation (S-LMF) was tested against five baseline algorithms using publicly available data sets from Yelp and Gowalla. Results demonstrated that S-LMF outperformed the best baseline model by improving @20 by 22.95

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Cite this article

@article{cai-7152,
  author  = {Qian Wang and Jiayin Wei and Lin Yao and Youjun Lu and Fujian Feng and Dan Peng},
  title   = {A Travel Point-of-Interest Recommendation Algorithm Incorporating Social Features and Logistic Matrix Factorisation},
  journal = {Computing and Informatics},
  volume  = {44},
  number  = {6},
  pages   = {1368--1392},
  year    = {2025},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2025_6_1368}
}
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