Personalized API recommendation via implicit preference modeling

With a huge amount of APIs on the Internet, understanding users' complex needs and preferences for APIs becomes an important task. In this paper, we aim to uncover users' implicit needs for APIs and recommend suitable APIs for users. Specifically, first different similarity scores between APIs are computed according to heterogeneous functional aspects of APIs. Next, users' preferences for APIs is combined with similarities of APIs measured with different functional aspects, and matrix factorization technique is used to learn the latent representation of users and APIs for each functional aspect. Then we use a personalized weight learning approach to combine the latent factors of different aspects to get the predicted preferences of users for APIs.