A Recommendation Method for Point-of-Interest Partition Based on Category Transfer Weighted Tensor Decomposition Model

LI Sheng; LIU Guiyun; HE Xiongxiong

doi:10.11999/JEIT200934

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 203-210

LI Sheng, LIU Guiyun, HE Xiongxiong. A Recommendation Method for Point-of-Interest Partition Based on Category Transfer Weighted Tensor Decomposition Model[J]. Journal of Electronics & Information Technology, 2022, 44(1): 203-210. doi: 10.11999/JEIT200934

Citation:

LI Sheng, LIU Guiyun, HE Xiongxiong. A Recommendation Method for Point-of-Interest Partition Based on Category Transfer Weighted Tensor Decomposition Model[J]. Journal of Electronics & Information Technology, 2022, 44(1): 203-210. doi: 10.11999/JEIT200934

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A Recommendation Method for Point-of-Interest Partition Based on Category Transfer Weighted Tensor Decomposition Model

doi: 10.11999/JEIT200934

School of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Funds: The National Natural Science Foundation of China (61873239,61675183), The Key R&D Projects in Zhejiang Province (2020C03074)

Received Date: 2020-11-02
Rev Recd Date: 2021-04-19

Available Online: 2021-08-26

Publish Date: 2022-01-10

Abstract

Abstract

Point-Of-Interest (POI) recommendation in location-based social networks is an important way for people to find interesting locations. However, in reality, both the various user preference of locations in different regions and the high-dimensional historical check-in information make accurate and personalized POI recommendations extremely challenging. In this regard, a new type of recommendation algorithm for point-of-interest Partition Recommendation based on a category transfer Weighted Tensor Decomposition (WTD-PR) model is proposed. The proposed algorithm makes full use of the user’s historical visit information by combining the user’s continuous behavior and time characteristics to obtain the category transfer weight factor; Then, by improving the user-time-category tensor model and adding the category transfer weight to the tensor to predict the user’s preference category; Finally, the local and remote locations are divided according to the user’s historical access area, and the recommended areas are found based on the user’s current location. After that, location and social factors are introduced and combined with the candidate categories to make the recommendation of points of interest. Through comparative experiments on real data sets, the proposed algorithm is proved not only to be universal, but also superior to other comparison algorithms in terms of recommendation performance.
- Point-Of-Interest (POI) recommendation,
- Tensor decomposition,
- Category transfer weight,
- Partition recommendation

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References(18)

References

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