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.
Highlights
In recent years, the rapid growth of intelligence terminal has inspired the development of application (APP) market
In order to guarantee fairness during APP recommendation process, the APP candidates are divided into high visibility APPs and low visibility APPs, and recommendations are implemented respectively
APP candidates are divided into high visibility APPs and low visibility APPs, and implement recommendation algorithms, respectively
Summary
The rapid growth of intelligence terminal has inspired the development of application (APP) market. Some low visibility APPs which lack of sufficient historical evaluation information, can not get a fair chance in these classical recommendation approaches. In reality, these low visibility APPs may satisfy requirements of users. Cold start technology employs similarity measurement and other approaches to help newborn APPs get QoS prediction It can not provide a relatively fair opportunity for newborn APPs and low frequency APPs. it can not provide a relatively fair opportunity for newborn APPs and low frequency APPs To this end, we present a fairness-aware APP recommendation method named FARM.
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