Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data

Abstract

Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN). Second, the improved Continuous Bags-of-Words (CBOW) model is used to extract the semantic features in the user comment information, and realize the implicit vector representation of POI in geographic, category, semantic and temporal feature space. Finally, by adaptively selecting relevant check-in activities from the check-in history to learn and change user preferences, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) can distinguish the user preferences of different check-in. Experiments show that when the length of the recommendation list is 15, the precision of the proposed algorithm on the loc-Gowalla data set is 0.0686, the recall is 0.0769, and the precision on the loc-Brightkite data set is 0.0659, the recall is 0.0835, both of which are better than the comparative recommendation methods. Therefore, compared with the comparison methods, the proposed method can significantly improve the performance of the POI recommendation system.

Performance, privacy and security issues at the TCP internet layer

“…Location Privacy: Location-based services raise concerns about the disclosure of users' physical whereabouts. TCP location-based services leverage the Transmission Control Protocol (TCP) along with geographical data and IP address mapping to deliver content and services tailored to the user's physical location [52], [53]. These services encompass a wide range of applications, from location-specific advertising and personalized content delivery to local weather forecasts and navigation aids.…”

Deep Learning-Based Recommendation System: Systematic Review and Classification

“…Graph-based methods for community detection and node embedding [103]- [106]; Graph neural network for interest data [16], [102], [103].…”

“…In today's business world, data-driven decisions are crucial, and many companies are incorporating recommendation system features into their websites and apps to enhance user experience and increase revenue [1]- [5]. The purpose of recommendation systems is to provide personalized and relevant suggestions to users based on their past behavior, preferences, and interests [6]- [9], and to solve the problem of information overload in various domains such as e-commerce [10]- [12], e-learning [13]- [15], social networks [16]- [21], and enter-tainment [22]- [25].…”

Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models

“…Consequently, traditional RNNs excel at solving short-term dependencies. Advanced architectures such as the Long Short-Term Memory (LSTM) [ 36 , 37 , 38 ] and the Gated Recurrent Unit (GRU) [ 39 , 40 , 41 , 42 ] have been developed to address this. LSTM employs memory cells and gating mechanisms to filter out the noise and selectively capture long-term dependencies more efficiently.…”