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.
Highlights
Personalized recommendation system actively provides users with information services that best meet their interests by capturing the users’ behavior preferences and information requirements (Sang et al, 2006)
We propose a P3OI recommendation algorithm based on multiexploring locality sensitive hashing in this paper
We study a MELSH algorithm based on the concept of multiple exploration
Summary
Personalized recommendation system actively provides users with information services that best meet their interests by capturing the users’ behavior preferences and information requirements (Sang et al, 2006). In location-based social network (LBSN), it becomes an important research to conduct point of interest (POI) recommendation based on the checkin data of users (Li, 2015; Xindi et al, 2019). Check-in data reflect users’ preferences for locations, which provides a solid basis for personalized POI recommendation. This recommendation method allows users to search new relevant places without spending much time, and allows service providers to provide accurate recommendation services to users
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