Integrating Geographical and Functional Relevance to Implicit Data for Web Service Recommendation

• Published in: Service-Oriented Computing Vol. 11895; pp. 53 - 57
• Main Authors: Botangen, Khavee Agustus, Yu, Jian, Yongchareon, Sira, Yang, LiangHuai, Sheng, Quan Z.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Implicit feedback; Location; Matrix factorization; Recommendation; Topic model
• ISBN: 3030337014; 9783030337018
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-33702-5_4

Designing efficient and effective Web service recommendation, primarily based on usage feedback, has become an important task to support the prevalent consumption of services. In the mashup-API invocation scenario, the most available feedback is the implicit invocation data, i.e., the binary data indicating whether or not a mashup has invoked an API. Hence, various efforts are exploiting potential impact factors to augment the implicit invocation data with the aim to improve service recommendation performance. One significant factor affecting the context of Web service invocations is geographical location, however, it has been given less attention in the implicit-based service recommendation. In this paper, we propose a recommendation approach that derives a contextual preference score from geographical location information and functionality descriptions. The preference score complements the mashup-API invocation data for our implicit-tailored matrix factorization recommendation model. Evaluation results show that augmenting the implicit data with geographical location information and functionality description significantly increases the precision of API recommendation for mashup services.