TY - GEN
T1 - Integrating geographical and functional relevance to implicit data for web service recommendation
AU - Botangen, Khavee Agustus
AU - Yu, Jian
AU - Yongchareon, Sira
AU - Yang, Liang Huai
AU - Sheng, Quan Z.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Implicit feedback
KW - Location
KW - Matrix factorization
KW - Recommendation
KW - Topic model
UR - https://www.scopus.com/pages/publications/85076344848
U2 - 10.1007/978-3-030-33702-5_4
DO - 10.1007/978-3-030-33702-5_4
M3 - Conference proceeding contribution
AN - SCOPUS:85076344848
SN - 9783030337018
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 57
BT - Service-Oriented Computing
A2 - Yangui, Sami
A2 - Bouassida Rodriguez, Ismael
A2 - Drira, Khalil
A2 - Tari, Zahir
PB - Springer
CY - Switzerland
T2 - 17th International Conference on Service-Oriented Computing, ICSOC 2019
Y2 - 28 October 2019 through 31 October 2019
ER -