Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation
Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation
复制标题
用于 Web 服务推荐的位置感知和个性化协同过滤
DOI：
10.1109/tsc.2015.2433251
复制
发表时间：
2016-09-01
影响因子：
8.1
通讯作者：
Lyu, Saixia
中科院分区：
文献类型：
--
作者：
Liu, Jianxun;Tang, Mingdong;Lyu, Saixia
协同过滤 (CF) 广泛用于 Web 服务推荐。基于CF的Web服务推荐旨在预测Web服务缺失的QoS（服务质量）值。尽管近年来已经提出了几种基于CF的Web服务QoS预测方法，但其性能仍然需要显着的改进。首先，现有的QoS预测方法在衡量用户之间和服务之间的相似性时很少考虑用户和服务的个性化影响。其次，Web 服务的 QoS 因素（例如响应时间和吞吐量）通常取决于 Web 服务和用户的位置。然而，现有的 Web 服务 QoS 预测方法很少考虑这一观察结果。在本文中，我们提出了一种用于 Web 服务推荐的位置感知个性化 CF 方法。所提出的方法在为目标用户或服务选择相似的邻居时利用用户和 Web 服务的位置。该方法还包括通过考虑用户和 Web 服务的个性化影响来增强对用户和 Web 服务的相似性测量。为了评估我们提出的方法的性能，我们使用真实世界的 Web 服务数据集进行了一组综合实验。实验结果表明，与之前基于 CF 的方法相比，我们的方法显着提高了 QoS 预测精度和计算效率。
Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Second, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.