Abstract
Service recommendation facilitates developers to select services to create new mashups with large-granularity and added value. Currently, most studies concentrate on mining and recommending common composition patterns in mashups. However, latent negative patterns in mashups, which indicate the inappropriate combinations of services, remain largely ignored. By combining additional negative patterns between services with the already-exploited common mashup patterns, we present a more comprehensive and accurate model for service recommendation. Both positive association rules and negative ones are mined from services’ annotated tags to predict future mashups. The extensive experiment conducted on real-world data sets shows a 33% enhancement in terms of F1-Score compared to classic association mining approach.
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Ni, Y., Fan, Y., Huang, K., Bi, J., Tan, W. (2014). Negative-Connection-Aware Tag-Based Association Mining and Service Recommendation. In: Franch, X., Ghose, A.K., Lewis, G.A., Bhiri, S. (eds) Service-Oriented Computing. ICSOC 2014. Lecture Notes in Computer Science, vol 8831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45391-9_32
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DOI: https://doi.org/10.1007/978-3-662-45391-9_32
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