Content based filtering for application software

In the study, two methods for recommending application software were implemented and evaluated based on their ability to recommend alternative applications with related functionality to the one that a user is currently browsing. One method was based on Term Frequency–Inverse Document Frequency (TF-IDF) and the other was based on Latent Semantic Indexing (LSI). The dataset used was a set of 2501 articles from Wikipedia, each describing a distinct application. Two experiments were performed to evaluate the methods. The first experiment consisted of measuring to what extent the recommendations for an application belong to the same software category, and the second was a set of structured interviews in which recommendations for a subset of the applications in the dataset were evaluated more in-depth. The results from the two experiments showed only a small difference between the methods, with a slight advantage to LSI for smaller sets of recommendations retrieved, and an advantage for TF-IDF for larger sets of recommendations retrieved. The interviews indicated that the recommendations from when LSI was used to a higher extent had a similar functionality as the evaluated applications. The recommendations from when TF-IDF was used had a higher fraction of applications with functionality that complemented or enhanced the functionality of the evaluated applications.