Abstract:
With the number of mobile applications available rapidly rising, users must exert considerable effort to
identify applications of interest. The goal of this thesis is to look at how to make suggestions to users to
help them find new desktop or mobile applications. The next step is to construct a proof-of-concept
system. The thesis's work is separated into three phases, with the first phase's goal being to research
similar work and related systems in order to pinpoint intriguing concepts and features. A prototype
system is created and put into use in the second phase. The third and last step then evaluates and analyses
the end and result of the preceding two phases. A desktop application of an employee recommendation
predictor built using machine learning models has been developed. The system will accurately determine
an employee's qualifying status for promotion. This Merit Based Promotion Recommendation System
software can be used to assist a firm department when the number of employees surpasses a significant
level. It will help HR navigate through vast databases and catalogues, our recommendation system will
suggest the deserving candidates for advancement. By filtering and recommending pertinent items while
taking into account or assuming the preferences of the users (i.e., experience, talents, or qualification)
based on these goals for merit-based promotion recommendation. This recommender system is based on
computer-based methods that can be used in numerous HR departments of organizations to effectively
give personalized services.
