The author interest discovery can help personalized academic recommendation systems. However, many topic models for discovering author interest implicitly assume equal contribution from each coauthor to a target document. To loosen this limitation, a novel model, ATcredit, is proposed to strengthen the Author-Topic (AT) model with an authorship credit allocation scheme, and the collapsed Gibbs sampling is utilized to approximate the posterior and estimate the model parameters. In total, our model considers six counting schemes, including fixed and flexible versions, as well as equal contributors and hyper-authorship strategies.