P3OI-MELSH: Privacy Protection Target Point of Interest Recommendation Algorithm Based on Multi-Exploring Locality Sensitive Hashing

Abstract

With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by users in the era of big data, improve user experience, but also help merchants quickly find target users and achieve accurate marketing. Most of the works are based on users' check-in history and social network data to model users' personalized preferences for interest points, and recommend interest points through collaborative filtering and other recommendation technologies. However, in the check-in history, the multi-source heterogeneous information (including the position, category, popularity, social, reviews) describes user activity from different aspects which hides people's life style and personal preference. However, the above methods do not fully consider these factors' combined action. Considering the data privacy, it is difficult for individuals to share data with others with similar preferences. In this paper, we propose a privacy protection point of interest recommendation algorithm based on multi-exploring locality sensitive hashing (LSH). This algorithm studies the POI recommendation problem under distributed system. This paper introduces a multi-exploring method to improve the LSH algorithm. On the one hand, it reduces the number of hash tables to decrease the memory overhead; On the other hand, the retrieval range on each hash table is increased to reduce the time retrieval overhead. Meanwhile, the retrieval quality is similar to the original algorithm. The proposed method uses modified LSH and homomorphic encryption technology to assist POI recommendation which can ensure the accuracy, privacy and efficiency of the recommendation algorithm, and it verifies feasibility through experiments on real data sets. In terms of root mean square error (RMSE), mean absolute error (MAE) and running time, the proposed method has a competitive advantage.

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

“…Then the classical recommendation algorithm was enhanced by considering the sparse data and strong correlation with geographical location. 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).…”

Criminalization of Copyright-Infringing Information Distribution Algorithms

“…IDA collects and analyses vast amounts of user usage data to recommend content that users may enjoy, hence boosting user retention or business conversion efficiency. [14] While the IDA provides users with highly tailored material, it also arouses concerns about the potential infringement of users' privacy rights security [15][16] and safety concerns about the algorithmic ethics of "information cocoons" [17]. Intellectual property infringement issues have also emerged, particularly when an ISP recommends UGC protected by copyright law to others without the consent of the cyber-copyright owners [18][19][20].…”

Research of Art Point of Interest Recommendation Algorithm Based on Modified VGG-16 Network

“…One obvious advance in social networking applications in recent years has been the introduction of spatial technology [1], which has facilitated the flourishing of location-based social networks (LBSN) such as Foursquare, Twinkle and Geolife. In LBSN, locations based on spatial technology are also called point-of-interest (POI), such as restaurants, shops and museums [2,3]. However, the interest point recommendation faces many problems, such as sparse matrix, low recommendation accuracy and low model performance.…”