FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9134908/ |
| _version_ | 1818857180468609024 |
|---|---|
| author | Qiliang Zhu Qibo Sun Zengxiang Li Shangguang Wang |
| author_facet | Qiliang Zhu Qibo Sun Zengxiang Li Shangguang Wang |
| author_sort | Qiliang Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T08:36:18Z |
| format | Article |
| id | doaj.art-039010cb32b2473091568d4c76895669 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:36:18Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-039010cb32b2473091568d4c768956692022-12-21T20:29:02ZengIEEEIEEE Access2169-35362020-01-01812274712275610.1109/ACCESS.2020.30076179134908FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPsQiliang Zhu0https://orcid.org/0000-0002-7977-9672Qibo Sun1Zengxiang Li2Shangguang Wang3https://orcid.org/0000-0001-7245-1298College of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaInstitute of High Performance Computing, Agency for Science Technology and Research (A*STAR), SingaporeState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaThe 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.https://ieeexplore.ieee.org/document/9134908/APP recommendationfairnessroulette-wheelfuzzy analytic hierarchy process |
| spellingShingle | Qiliang Zhu Qibo Sun Zengxiang Li Shangguang Wang FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs IEEE Access APP recommendation fairness roulette-wheel fuzzy analytic hierarchy process |
| title | FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs |
| title_full | FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs |
| title_fullStr | FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs |
| title_full_unstemmed | FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs |
| title_short | FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs |
| title_sort | farm a fairness aware recommendation method for high visibility and low visibility mobile apps |
| topic | APP recommendation fairness roulette-wheel fuzzy analytic hierarchy process |
| url | https://ieeexplore.ieee.org/document/9134908/ |
| work_keys_str_mv | AT qiliangzhu farmafairnessawarerecommendationmethodforhighvisibilityandlowvisibilitymobileapps AT qibosun farmafairnessawarerecommendationmethodforhighvisibilityandlowvisibilitymobileapps AT zengxiangli farmafairnessawarerecommendationmethodforhighvisibilityandlowvisibilitymobileapps AT shangguangwang farmafairnessawarerecommendationmethodforhighvisibilityandlowvisibilitymobileapps |