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.
Developers use software information sites such as Stack Overflow to get and give information on various subjects. These sites allow developers to label content with tags as a short description. Tags, then, are used to describe, categorize and search the posted content. However, tags might be noisy, and postings may become poorly categorized since people tag a posting based on their knowledge of its content and other existing tags. To keep the content well organized, tag recommendation systems can help users by suggesting appropriate tags for their posted content. In this paper, we propose a tag recommendation scheme that uses the textual content of already tagged postings to recommend suitable tags for newly posted content. Our approach combines multi-label classification and textual similarity techniques to improve the performance of tag recommendation. We evaluate the performance of the proposed scheme on 11 software information sites from the Stack Exchange network. The results show a significant improvement over TagCombine, TagMulRec and FastTagRec, which are well-known tag recommendation systems. On average, the proposed model outperforms TagCombine, TagMulRec and FastTagRec by 26.2, 15.9 and 13.8% in terms of Recall@5 and by 16.9, 12.4 and 9.4% in terms of Recall@10, respectively.
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