Liu Desheng, Shan Linna, Wang Lei, Yin Shoulin, Wang Hui, Wang Chaoyang
College of Information and Electronic Technology, Jiamusi University, Jimusi, China.
Software College, Shenyang Normal University, Shenyang, China.
Front Neurorobot. 2021 Apr 23;15:660304. doi: 10.3389/fnbot.2021.660304. eCollection 2021.
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.
随着社交网络、智能终端和自动定位技术的快速发展，基于位置的社交网络（LBSN）服务已成为一项重要且有价值的应用。兴趣点（POI）推荐是LBSN中的一项重要内容，其目的是为用户推荐新的感兴趣地点。它不仅可以缓解用户在大数据时代面临的信息过载问题，提升用户体验，还能帮助商家快速找到目标用户并实现精准营销。大多数工作基于用户的签到历史和社交网络数据来建模用户对兴趣点的个性化偏好，并通过协同过滤等推荐技术推荐兴趣点。然而，在签到历史中，多源异构信息（包括位置、类别、热度、社交、评论）从不同方面描述了用户活动，其中隐藏着人们的生活方式和个人偏好。然而，上述方法并未充分考虑这些因素的综合作用。考虑到数据隐私，个人很难与有相似偏好的其他人共享数据。在本文中，我们提出了一种基于多探索局部敏感哈希（LSH）的隐私保护兴趣点推荐算法。该算法研究分布式系统下的POI推荐问题。本文引入一种多探索方法来改进LSH算法。一方面，减少哈希表数量以降低内存开销；另一方面，增加每个哈希表上的检索范围以减少时间检索开销。同时，检索质量与原算法相似。所提方法使用改进的LSH和同态加密技术辅助POI推荐，这可以确保推荐算法的准确性、隐私性和效率，并通过在真实数据集上的实验验证了其可行性。在均方根误差（RMSE）、平均绝对误差（MAE）和运行时间方面，所提方法具有竞争优势。
