Location
Level 0, Open Space, Owen G. Glenn Building
Start Date
12-15-2014
Description
The explosive growth of mobile apps makes it difficult for users to find their needed apps in a crowded market. Effective mechanism that provides high quality app recommendations becomes necessary. However, existing recommendation techniques tend to recommend similar items but fail to consider users’ functional requirements, making them not effective in app domain. In this paper, we propose a recommendation architecture that is able to generate app recommendations at functionality level. We address the redundant recommendation problem in app domain by highlighting users’ functional requirements that have received scant attention from existing recommendation research. Another main feature of our work is extracting app functionalities from textural user reviews for recommendation. Effective approach for functionality extraction is also proposed.
Recommended Citation
Xu, Xiaoying; Dutta, Kaushik; and Datta, Anindya, "Functionality-Based Mobile App Recommendation by Identifying Aspects from User Reviews" (2014). ICIS 2014 Proceedings. 12.
https://aisel.aisnet.org/icis2014/proceedings/DecisionAnalytics/12
Functionality-Based Mobile App Recommendation by Identifying Aspects from User Reviews
Level 0, Open Space, Owen G. Glenn Building
The explosive growth of mobile apps makes it difficult for users to find their needed apps in a crowded market. Effective mechanism that provides high quality app recommendations becomes necessary. However, existing recommendation techniques tend to recommend similar items but fail to consider users’ functional requirements, making them not effective in app domain. In this paper, we propose a recommendation architecture that is able to generate app recommendations at functionality level. We address the redundant recommendation problem in app domain by highlighting users’ functional requirements that have received scant attention from existing recommendation research. Another main feature of our work is extracting app functionalities from textural user reviews for recommendation. Effective approach for functionality extraction is also proposed.