Collaborative Filtering Software

Software engineers have to select some appropriate development technologies to use in the work; however, engineers sometimes cannot find the appropriate technologies because there are vast amount of options today. To solve this problem, we propose a software technology recommendation method based on collaborative filtering (CF). In the proposed method, at first, questionnaires are collected from concerned engineers about their technical interest. Next, similarities between an active engineer who gets recommendation and the other engineers are calculated according to the technical interests. Then, some similar engineers are selected for the active engineer.

At last, some technologies are recommended which attract the similar engineers. An experimental evaluation showed that the proposed method can make accurate recommendations than that of a naive (non-CF) method.

Collaborative filtering of information

While generalized, or aggregate, information is essential for balancing social processes on the macro-scale, it is usually not sufficient for suggesting optimal behavior to any particular person. For making efficient personal selections, people have to possess both necessary general knowledge and special information relevant to their particular situation. For collecting necessary information, one has to choose what objects to pay attention to. In early human history, each person was familiar with the whole environment and, after gaining experience with most available things and people, could decide what to explore further.

Collaborative filtering is the process of predicting ratings based on a database of ratings from various users. It is widely applicable to e-Commerce, e-Learning, and so on.
Currently, programmers who want to use collaborative filtering have to read the literature and implement their own algorithms. More often than not, programmers probably design their own algorithms and they will generally produce suboptimal algorithms. We want to build a foundation of already tested algorithms and documented that can be used in a wide range of contexts from research to applications.