A Novel Gamification Approach to Recomendation Based Mobile Applications

Sites in E-Commerce tag like Amazon are now an integral section of the internet economy these days. Rapid growth in the smart phone market has excelled these sectors into the mainstream. There are many applications of recommender based system that is used by smartphone users such as e-commerce apps, travel apps and many others. The focus of this paper is to design an approach via data mining, human psychology techniques in interaction to make a mobile application profitable by making a large share of users tend to use the application for some significant time more and hence increase the company's revenue via advertisements or other means. This paper also focuses on increasing the usability to all the users of recommender based mobile applications and also on increasing the profit of the company. Integration of human computer interaction psychology and data mining with Natural Language Processing techniques helps in achieving the goal. Focus is made in generation of recommended results and its eventual generation of “wh” questions like who/what/when from the recommended object's property. A web scrapper is implemented to automatically fetch the recommended item's information from the web. Questions are generated as a set and ranked within the set. The question having the highest rank among all is then picked and linked to the notifications of the application at regular intervals. This notification linkage is done in accordance with the exploitation of the human mind's tendency of attention due to curiosity. Survey performed to find the improvement in the hit rate of the application and show the evidence and support of the motive.