POI Recommendation Model Using Multi-Head Attention in Location-Based Social Network Big Data

Abstract

A point of interest (POI) recommendation model using deep learning in location-based social network big data is proposed. Firstly, the features of POI are divided into inherent features composed of attributes such as geographical location and category, and semantic features of relevance composed of spontaneous access by users. Secondly, the inherent attribute features and semantic features of POI are extracted by constraint matrix decomposition and word vector model respectively, and the two hidden vectors are spliced into the feature vectors of POI to solve the problems of data sparsity and cold start. Finally, the multi-head attention is used to obtain the key information of user preferences, and a deep learning recommendation framework is constructed to model the nonlinear interaction between features. Experiments show that when the recommendation list is 10, the precision and recall of the proposed method are 0.118 and 0.135 respectively, which are better than the comparative recommendation method.

Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN

“…It incorporates geographical influence and POI categories to effectively acquire the correlation between different POIs. Lai and Zeng (2023) utilized attention-mechanism modules and long short-term memory (LSTM) to capture sequence features and short-term preferences of historical data. Liu (2023) utilized multi-head attention to obtain userpreference information and modeled the nonlinear interaction between multidimensional features.…”

“…Lai and Zeng (2023) utilized attention-mechanism modules and long short-term memory (LSTM) to capture sequence features and short-term preferences of historical data. Liu (2023) utilized multi-head attention to obtain userpreference information and modeled the nonlinear interaction between multidimensional features. Jia (2023) combined time-series features with distance-context features, effectively alleviating the sparsity of data.…”

“…A recommendation model has been constructed based on the characteristics of point-of-interest recommendation data (Lim et al, 2019;Liu et al, 2021). The deep-learning method relies on a substantial volume of data to train the model and extract valuable insights (Abbasi-Moud et al, 2021;Liu, 2023). Hence, deep learning possesses the capability to more effectively extract information and comprehend the complex relationships among various aspects.…”

“…Location-based social networks (LBSNs) have attracted a large number of users (Werneck et al, 2021). Users share their travel photos or check-in data through social networks to record access history and share life experiences, thus accumulating a large number of access footprints or check-in record data with geographical markers (Lai & Zeng, 2023;Liu, 2018;Xing et al, 2019). The historical access data of these users provide an opportunity to get insights into people's behavior and can be effectively utilized for personalized point-of-interest suggestion via social networks (Chakraborty et al, 2020;Mishra et al, 2020;Xing et al, 2018;Yang et al, 2019).…”

A Fine-Grained Sentiment Analysis Method Using Transformer for Weibo Comment Text

“…New media platforms like Weibo have become essential for capturing and shaping public opinion. These platforms provide a breeding ground for online discussions, where public sentiment surrounding current events can simmer, spread, and erupt (Liao et al, 2022;Lin et al, 2020;Liu, 2023;. Netizens comment on events in the news through Weibo and express, disseminate, and interact with their emotions, thereby forming public opinion on these events (Fang et al, 2023;Yu et al, 2021;Zhu et al, 2022).…”