PCNet: A Personalized Complementary Network via Tensor Decomposition for Service Recommendation

Web services are widely utilized across domains such as cloud computing, mobile networks, and Web applications. Due to their single-function nature, these services are often composed into Mashups to achieve more comprehensive functionality. However, the rapid growth in the number and variety of Web services has made it increasingly difficult to identify suitable services for Mashup development. Web service recommendation systems have emerged as a solution to this service overload, supporting innovative practices within the service-oriented development paradigm. While existing methods emphasize recommendation accuracy and relevance, few approaches simultaneously consider the personalized requirements of the Mashup side and the complementary relationships on the service side, both of which are essential for reconstructing the Web service ecosystem’s value chain. To address this gap, we propose PCNet, a Personalized Complementary Network for service recommendation based on tensor decomposition. We conceptualize the interaction dynamics between Mashups and services, as well as coinvocation patterns among services, using a three-dimensional tensor. The RESCAL tensor decomposition technique is then applied to jointly learn these relationships and uncover personalized complementary relationships among services. In addition, we develop a complementary perception module that uses an attention mechanism to dynamically model a Mashup’s focus on different complementary relationships, extending them to higher orders. Experimental results on real-world Web service datasets demonstrate that PCNet significantly outperforms state-of-the-art baselines. The implementation of PCNet is publicly available at: https://github.com/MengMeng3399/PCNet.