[1]TU Fei.A point of interest recommendation algorithm based on multi-feature fusion[J].CAAI Transactions on Intelligent Systems,2019,14(4):779-786.[doi:10.11992/tis.201801048]
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A point of interest recommendation algorithm based on multi-feature fusion
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 4
Page number:
779-786
Column:
学术论文—机器学习
Public date:
2019-07-02
- Title:
- A point of interest recommendation algorithm based on multi-feature fusion
- Keywords:
- location-based social networks; point of interest recommendation; topic model; perplexity; sparseness; aggregation; collaborative filtering; multi-feature fusion
- CLC:
- TP391.9
- DOI:
- 10.11992/tis.201801048
- Abstract:
- The point of interest recommendation service is receiving increasing attention from the industry and academia. The sparsity of users’ activity history datasets and aggregation of geological position prevent the current recommendation algorithm efficiency from being high, and especially, when a user goes out to a new city, the recommendation effect will fall sharply. Therefore, this paper presents a user-content-region topic model based on a joint recommendation algorithm, considering to the user’s preferences, the content of the point of interest, and the geographical area, making users obtain an ideal recommendation service irrespective of their location. An experiment was carried out on two real datasets, and the results show that this method can not only overcome problems such as data sparseness, weak semantic performance, but also has a higher recommendation accuracy compared with other methods.
- References:
-
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Last Update:
2019-08-25