FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs

Abstract

The number of mobile applications(APPs) has increased dramatically with the development of mobile Internet. It becomes challenging for users to identify these APPs they are really interested in. Existing mobile APP recommendation methods focus on learning users' preference and recommending high visibility APPs. However, some low visibility APPs may satisfy users and even surprise them. If those low visibility APPs have the opportunity to show to the user, they will not only improve the user's satisfaction, but also provide a fair competitive market for APP providers. Furthermore, it will improve the vitality of the APP market. To this end, we present a fairness-aware APP recommendation method named FARM. The principal study of this method emphasizes on the fairness issue during the recommendation process. In this method, APP candidates are divided into high visibility and low visibility APPs, and implement recommendation algorithm respectively. For low visibility APPs, we set a fairness factor for everyone, and use the user's latest feedback to make a dynamic adjustment. Based on the fairness factor, the recommendation is implemented by roulette-wheel. For high visibility APPs, we employ the fuzzy analytic hierarchy process to implement the recommendation. The evaluation results show that FARM outperforms baselines in terms of recommendation fairness.

The Sustainability of Superior Performance of Platform Complementor: Evidence from the Effects of Iterative Innovation and Visibility of App in iOS Platform in China

“…Third, this study demonstrates the limitation of considering app visibility as a concept with a single dimension [4][5][6]. Choi et al believed that higher ranking of app list led to higher visibility [4], while Zhu et al argued that platforms recommendation increased visibility by enhancing app's exposure to users [5]. we propose that platform recommendation mechanisms and users' download rankings are two important and substantially different dimensions of app visibility.…”

“…Third, this study demonstrates the limitation of considering app visibility as a concept with a single dimension [4][5][6]. Choi et al believed that higher ranking of app list led to higher visibility [4], while Zhu et al argued that platforms recommendation increased visibility by enhancing app's exposure to users [5].…”

“…This study makes the following theoretical contributions. First, by showing how app, as the platform complementor, can shape its sustainability of superior performance, we depart from the existing treatments of app's direct effect [3,5]. Choi studies the direct impact of the freemium strategy on the increased sales of the paid mobile apps, and obtains the positive effects [4].…”

“…Here, app visibility refers to the possibility that a potential user might discover the app [4]. The literature on app visibility is based mainly on market phenomena and focuses on the contradiction between the explosive growth of apps and the scarcity of user attention [5]. It also addresses the resulting increase in user search costs and the challenge of accumulating users from scratch.…”

A Survey on the Fairness of Recommender Systems

“…Jain's index [44] is commonly used to measure unfairness in network engineering. Some studies use it to measure the inconsistency of predicted user satisfaction in group recommendations [105] and the inconsistency of item exposure [118]. The higher the value, the fairer the recommendations.…”