Since many of today's application software provide users with too many functions, the users sometimes cannot find the useful functions. This paper proposes a recommendation system based on a collaborative filtering approach to let users discover useful functions at low cost for the purpose of improving the user's productivity in using application software. The proposed system automatically collects histories of software function execution (usage histories) from many users through the Internet. Based on the collaborative filtering approach, collected histories are used to recommend the user a set of candidate functions that may be useful to the individual user. This paper illustrates conventional filtering algorithms and proposes a new algorithm suitable for recommendation of software functions. The result of an experiment with a prototype recommendation system showed that the average napin of our algorithm was smaller,than that of the conventional algorithms; and, it also showed that the standard deviation of ndpm of our algorithm was smaller than that of the conventional algorithms. Furthermore, while every conventional algorithm had a case whose recommendation was worse than the random algorithm, our algorithm did not.