Abstract
To solve the problems of large data sparsity and lack of negative samples in most point of interest (POI) recommendation methods, a POI recommendation method based on deep learning in location‐based social networks is proposed. Firstly, a bidirectional long‐short‐term memory (Bi‐LSTM) attention mechanism is designed to give different weights to different parts of the current sequence according to users’ long‐term and short‐term preferences. Then, the POI recommendation model is constructed, the sequence state data of the encoder is input into Bi‐LSTM‐Attention to get the attention representation of the current POI check‐in sequence, and the Top‐N recommendation list is generated after the decoder processing. Finally, a negative sampling method is proposed to obtain an effective negative sample set, which is used to improve the calculation of the Bayesian personalized ranking loss function. The proposed method is demonstrated experimentally on Foursquare and Gowalla datasets. The experimental results show that the proposed method has better accuracy, recall, and F1 value than other comparison methods.
Highlights
With the maturity of internet technology and the widespread application of global satellite positioning systems, locationbased social network (LBSN) has gradually received more and more attention and research
In order to solve the above-mentioned problems of user dynamic preferences and data sparsity, a points of interest (POI) recommendation method based on deep learning in LBSN is proposed
In order to accurately evaluate the performance of the proposed method on the POI recommendation task, Precision is used to measure it, and the calculation is as follows: Precision
Summary
With the maturity of internet technology and the widespread application of global satellite positioning systems, locationbased social network (LBSN) has gradually received more and more attention and research. In order to solve the above-mentioned problems of user dynamic preferences and data sparsity, a POI recommendation method based on deep learning in LBSN is proposed. (1) Due to the temporal dynamics problem that the traditional recommendation method does not solve and the in-depth recommendation does not consider the attention problem To this end, the proposed method (Bi-LSTM-Attention) embeds a userlocation cross-attention mechanism layer in the neural network to capture effective contextual information (2) In order to better capture the long-term and shortterm preferences of users in the POI sign-in sequence, the proposed method introduces a BiLSTM-Attention mechanism. The long-term and short-term preferences of the user are mined from the user’s history and the current POI sign-in sequence It solves the problem of inaccurate recommendation caused by users’ dynamic preferences (3) Aiming at the problem of the lack of valid negative samples in the dataset, the proposed method combines the popularity sampling weight and the distance sampling weight in a weighted combination.
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