Counteracting Popularity Bias in Multimedia Web API Recommendation

Dengshuai Zhai, Chao Yan, Weiyi Zhong, Shaoqi Ding, Lianyong Qi, Xiaokang Zhou

Abstract: With the widespread adoption of multimedia web APIs (API) in web and mobile applications, a substantial proliferation of these APIs is observed. These APIs have streamlined development processes, reducing both time and costs. Nevertheless, identifying the required APIs from the vast array of options has emerged as a significant challenge. Collaborative filtering (CF)-based recommendation technologies have demonstrated their efficiency in presenting developers with potentially useful APIs. However, these methods often suffer from popularity bias, i.e., popular APIs tend to dominate the recommendation lists. This imbalance in recommendation opportunities among APIs hinders the growth of the multimedia API ecosystem. To mitigate the popularity bias produced by CF-based API recommendation methods, this article introduces a novel debiasing strategy that combines a log postprocessing adjustment (LPA) with determinant point process (DPP). Specifically, the LPA is employed during the prediction phase to yield a more balanced set of candidate APIs. Then, DPP is utilized to generate recommendation lists that are not just relevant but also diverse in terms of API popularity. Experimental results reveal that our proposed method surpasses existing state-of-the-art approaches in multimedia API recommendation, excelling in both accuracy and the capability to mitigate popularity bias effectively.

External IDs:dblp:journals/tcss/ZhaiYZDQZ25