A Comparative Study of People-to-People Recommender Algorithms in Hybrid Method

Based on Internet Live Stats data, more than 2 billions information has been accessed daily of internet user daily. Amatriain said that trend of searching method has been deprecated. Trend of recommendation method has taken the position nowadays. Information must been processed to be transformed into recommendation and recommendation will reveal hidden information to the right position. One of recommendation method is people-to-people recommendation because one of the most accessed media in internet is social media. This research will discuss about comparison between hybrid algorithm in people-to-people recommendation. There are three algorithms which will be compared: hybrid content-collaborative reciprocal (without using weight) algorithm, hybrid contentcollaborative reciprocal (using weight) algorithm, and interactive-based + decision tree algorithm. These algorithm will be implemented in recommending workout partner using “FitParners”, Android-based mobile application and used 200 respondents (active or not active workout). The result will be compared based on execution time in generating the result and accuracy level of recomendation . Interactive-based + decision tree algorithm has best execution time which is 1726 ms. Hybrid content-collaborative reciprocal (using weight) algorithm has best accuracy level which is 76,45%.