A recommendation system for software function discovery

Since some application software provides users with too many functions, it is often difficult to find those that are useful. 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 productivity when 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 for recommending 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 ndpm of our algorithm was smaller than that of conventional algorithms, and it also showed that the standard deviation of ndpm of our algorithm was smaller than that of conventional algorithms. Furthermore, while every conventional algorithm had a case whose recommendation was worse than the random algorithm, our algorithm did not.