Collaborative filtering(CF)plays a key role in various rec-ommendation systems,but its effiectiveness will be limited by the highly sparse user-image click-through data when CF deploys for image recom-mendation applications.Some existing methods apply clustering tech-niques to mitigate the sparseness issue.However,there is still a big room to elevate the recommendation performance,because little is known in taking both click-through data and image visual information into accoun-t.In this paper,we propose an Asynchronously Bi-Clustering(ABC)CF approach to improve the CF-based image recommendation.Our ABC approach consists of two coupled clustering solutions.Concretely,it first implements image clustering based on image click-through and visual fea-ture,and then conducts user clustering in a low-dimensional subspace s-panned by the image clusters.The final recommendation is accomplished based on both user clusters and image clusters by a similarity fusion s-trategy.An empirical study shows that our ABC approach is bene cial to the CF-based image recommendation,and the proposed scheme is significantly more effective than some existing methods.