Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Facilitating innovation in the API economy
T2 - Privacy-enhanced and novelty-aware API recommendation for enterprises
AU - Xin, Baogui
AU - Yan, Chao
AU - Cao, Yuxuan
AU - Bilal, Muhammad
PY - 2023/7/1
Y1 - 2023/7/1
N2 - Web APIs provide enterprises with a new way of driving innovations of new technology with limited resources. API recommendations greatly alleviate the selection burdens of enterprises in identifying potential useful APIs to meet their business demands. However, these approaches disregard the privacy leakage risk in cross-platform collaboration and the popularity bias in recommendation. To address these issues, first, we introduce MinHash, an instance of locality-sensitive hashing, into a collaborative filtering technique and propose a novel, privacy-enhanced, API recommendation approach. Second, we present a simulation algorithm to analyze the popularity bias in API recommendation. Third, we mitigate popularity bias by improving the novelty of recommendation results with an adaptive reweighting mechanism. Last, comprehensive experiments are conducted on a real-world dataset collected from ProgrammableWeb. Experimental results show that our proposed approach can effectively preserve usage data privacy and mitigate popularity bias at a minimum cost in accuracy.
AB - Web APIs provide enterprises with a new way of driving innovations of new technology with limited resources. API recommendations greatly alleviate the selection burdens of enterprises in identifying potential useful APIs to meet their business demands. However, these approaches disregard the privacy leakage risk in cross-platform collaboration and the popularity bias in recommendation. To address these issues, first, we introduce MinHash, an instance of locality-sensitive hashing, into a collaborative filtering technique and propose a novel, privacy-enhanced, API recommendation approach. Second, we present a simulation algorithm to analyze the popularity bias in API recommendation. Third, we mitigate popularity bias by improving the novelty of recommendation results with an adaptive reweighting mechanism. Last, comprehensive experiments are conducted on a real-world dataset collected from ProgrammableWeb. Experimental results show that our proposed approach can effectively preserve usage data privacy and mitigate popularity bias at a minimum cost in accuracy.
KW - API recommendation
KW - Popularity bias
KW - Privacy preservation
KW - Recommendation novelty
U2 - 10.1016/j.jik.2023.100401
DO - 10.1016/j.jik.2023.100401
M3 - Journal article
AN - SCOPUS:85162098067
VL - 8
JO - Journal of Innovation and Knowledge
JF - Journal of Innovation and Knowledge
SN - 2530-7614
IS - 3
M1 - 100401
ER -