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User profiles in collaborative filtering (CF) recommendation technique are built based on ratings given by users on a set of items. The most eminent shortcoming of the CF technique is the sparsity problem. This problem refers to the low ratio of rated items by users to the total number of available items; hence the quality of recommendation will be affected. Most researchers use implicit data as a solution for sparsity problem, to decrease the dependency of CF technique on the user's rating and this term is more common in this field. The aim of this research is to aggregate evidence on state of research and practice of CF and implicit data applying systematic literature review (SLR) which is a method for evidence-based software engineering (EBSE). EBSE has the potential value for synthesizing evidence and make this evidence available to practitioners and researchers with providing the best references and appropriate software engineering solutions for sparsity problem. We executed the standard systematic literature review method using a manual search in 5 prestigious databases and 38 studies were finally included for analyzing. This paper follows manifestation of Kitchenham's SLR guidelines and describes in a great detail the process of selecting and analyzing research papers. This paper is first academic systematic literature review of CF technique along with implicit data from user behaviors and activities to aggregate existing evidence as a synthesis of best quality scientific studies. The 38 research papers are categorized into eleven application fields (movie, shopping, books, Social systems, music and others) and six data mining techniques (dimensionality reduction, association rule, heuristic methods and other). According to the review results, neighborhood formation is a relevant aspect of CF and it can be improved with the use of user-item preference matrix as implicit feedback mechanism, the most common domains of CF are in e-commerce and movie software applications.