Goal-Driven Context-Aware Next Service Recommendation for Mashup Composition

TL;DR

This paper introduces a novel context-aware, goal-driven recommendation system for suggesting the next web service in mashup development, leveraging service embeddings and goal exclusionary sampling to improve accuracy.

Contribution

It presents an incremental recommend-as-you-go approach with a new embedding learning algorithm and a goal exclusionary negative sampling mechanism for mashup service recommendation.

Findings

Effective in real-world datasets

Improves recommendation accuracy

Enhances mashup development efficiency

Abstract

As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services. To create a mashup, it gets not only time-consuming but also error-prone for developers to find suitable services from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected to the current step as well as its mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of our approach.