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Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios

机译：基于IOT场景的协作过滤的个性化推荐系统

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摘要

Recommendation technology is an important part of the Internet of Things (IoT) services, which can provide better service for users and help users get information anytime, anywhere. However, the traditional recommendation algorithms cannot meet user's fast and accurate recommended requirements in the IoT environment. In the face of a large-volume data, the method of finding neighborhood by comparing whole user information will result in a low recommendation efficiency. In addition, the traditional recommendation system ignores the inherent connection between user's preference and time. In reality, the interest of the user varies over time. Recommendation system should provide users accurate and fast with the change of time. To address this, we propose a novel recommendation model based on time correlation coefficient and an improved K-means with cuckoo search (CSK-means), called TCCF. The clustering method can cluster similar users together for further quick and accurate recommendation. Moreover, an effective and personalized recommendation model based on preference pattern (PTCCF) is designed to improve the quality of TCCF. It can provide a higher quality recommendation by analyzing the user's behaviors. The extensive experiments are conducted on two real datasets of MovieLens and Douban, and the precision of our model have improved about 5.2 percent compared with the MCoC model. Systematic experimental results have demonstrated our models TCCF and PTCCF are effective for IoT scenarios.
机译：推荐技术是事物互联网（物联网）服务的重要组成部分，它可以为用户提供更好的服务，并帮助用户随时随地获取信息。但是，传统推荐算法不能满足用户在物联网环境中的快速准确建议的要求。在大批量数据的面上，通过比较整个用户信息来查找邻域的方法将导致低推荐效率。此外，传统推荐系统忽略了用户偏好和时间之间的固有连接。实际上，用户的兴趣随着时间的推移而变化。推荐系统应为用户提供准确，快速的时间变化。为了解决这个问题，我们提出了一种基于时间相关系数的新推荐模型和具有CUCKOO搜索（CSK-MEARY）的改进的K型，称为TCCF。聚类方法可以将类似的用户聚集在一起，以进一步快速准确的推荐。此外，基于偏好模式（PTCCF）的有效和个性化推荐模型旨在提高TCCF的质量。它可以通过分析用户的行为来提供更高质量的推荐。广泛的实验是在Movielens和Douban的两个真实数据集上进行，与MCOC模型相比，我们模型的精度提高了约5.2％。系统实验结果表明，我们的模型TCCF和PTCCF对IOT场景有效。

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