Abstract: The online social communities employ several techniques to attract more users to their services. One of the essential demand of these communities is to find efficient ways to attract more users and improve their engagement. For this reason, social media sites typically take advantage of gamification systems to improve users’ participation. Among all the gamification services, badges are the most popular feature in online communities which are massively used as a reward system for users. Therefore, the recommendation of relevant unachieved badges to users will have a significant impact on their engagement level; instead of leaving them in the ocean of different actions and badges. In this paper, we develop a badge recommendation model based on item-based collaborative filtering which recommends the next achievable badges to users. The model calculates the correlation between unachieved badges and users’ previously awarded badges. We evaluate our model with the data from Stack Overflow question-answering website to examine if the recommendation model can recommend proper badges in an existing real community. Experimental results show that the model has about 70 per cent true recommendation by just recommending one badge and it has about 80 per cent correct recommendation if it recommends two badges for each user.

Reza Gharibi and Mohammad Malekzadeh, “Gamified Incentives: A Badge Recommendation Model to Improve User Engagement in Social Networking Websites” International Journal of Advanced Computer Science and Applications(IJACSA), 8(5), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080533

@article{Gharibi2017,
title = {Gamified Incentives: A Badge Recommendation Model to Improve User Engagement in Social Networking Websites},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080533},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080533},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {5},
author = {Reza Gharibi and Mohammad Malekzadeh}
}