Mashup Recommendation by Regularizing Matrix Factorization with API Co-Invocations

Abstract

Mashups are a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs, which are generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for their composition tasks. In particular, we propose a probabilistic matrix factorization approach with implicit correlation regularization to solve the recommendation problem and enhance the recommendation diversity. We conjecture that the co-invocation of APIs in real-world mashups is driven by both the explicit textual similarity and implicit correlations of APIs such as the similarity or the complementary relationship of APIs. We develop a latent variable model to uncover the latent correlations between APIs by analyzing their co-invocation patterns. We further explore the relationships of topics/categories to the proposed approach. We demonstrate the effectiveness of our approach by conducting extensive experiments on a real dataset crawled from ProgrammableWeb.

Automated Malware Detection in Mobile App Stores Based on Robust Feature Generation

“…Inverse Document Frequency (IDF) measures the importance of a feature by comparing its frequency of occurrence to those in other applications. TF-IDF is one of the best-known measures for specifying the weights [40]. The main objective of employing TF-IDF, as opposed to measuring the number of appearances, is to reduce the weight of features that appear frequently in many samples and increase the weight of features that appear less frequently in a small part of the training corpus.…”

Software expert discovery via knowledge domain embeddings in a collaborative network

“…Matrix factorization is a latent factors model, which to some extend can help with sparse data, which is widely used in industry, and adopted by many collaborative filtering recommendation systems (Koren et al (2009);Liang et al (2016); Yao et al (2015Yao et al ( , 2018b). It is also worth mentioning that a similar factorization technique, Tensor Decomposition, is also quite successful in these kind of applications (Yao et al (2018a); Huang et al (2018)).…”

Mashup-Oriented API Recommendation via Random Walk on Knowledge Graph

“…This ensures that the recommendation can be made within a comparable short list of interrelated services, with the latent relationship taken into account. Recently, Yao et al [8] propose a matrix factorization with implicit correlation regularization to solve the recommendation problem and enhance the recommendation diversity. They conjecture that the co-invocation of APIs in mashups is driven by both the explicit textual similarity and implicit correlations of APIs such as the similarity or the complementary relationship of APIs.…”

“…In recent years, numerous efforts have been made to address this issue. Existing works can be coarsely classified into two categories, one focuses on the principle of collaborative filtering [5]- [8], and the other focuses on estimating the relevance between the mashup requirements and the candidate APIs [9]- [13]. Various technologies, e.g., matrix factorization [7], [8], topic modeling [9], [10], link analysis [11] and various features, e.g., texts, tags, topics and popularity are exploited to enhance the accuracy of recommendations [13]- [17].…”