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• W2945973724 abstract "Location recommendation method is an important application in a location-based social network. At present, it is a trend to integrate different recommendation methods since they have their own advantages in capturing different preferences of users and an integrated method can generally provide a better performance than every individual. However, the existing integration policies do not learn user preferences in their integration processes so that they cannot make full use of the advantage of each method. Therefore, we propose a novel integration method: learning-to-rank-based integration . In our method, a confidence coefficient is applied for each user in the integration process, and these coefficients can well optimize recommendation performance. A learning-to-rank-based algorithm is designed to train the confidence coefficients. A group of experiments are done on a real large-scale check-in data set, and the results demonstrate that our method outperforms the state-of-the-art ones." @default.
• W2945973724 created "2019-05-29" @default.
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• W2945973724 date "2019-06-01" @default.
• W2945973724 modified "2023-10-10" @default.
• W2945973724 title "LORI: A Learning-to-Rank-Based Integration Method of Location Recommendation" @default.
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• W2945973724 doi "https://doi.org/10.1109/tcss.2019.2907563" @default.
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