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Content based filtering for application software
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Innehållsbaserad filtrering för applikationsprogramvara (Swedish)
Abstract [en]

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.

Abstract [sv]

I studien implementerades och utvärderades två alternativa implementationer av ett rekommendationssystem för applikationsprogramvara. Implementationerna utvärderades baserat på deras förmåga att föreslå alternativa applikationer med relaterad funktionalitet till den applikation som användaren av ett system besöker eller visar. Den ena implementationen baserades på Term Frequency-Inverse Document Frequency (TF-IDF) och den andra på Latent Semantic Indexing (LSI). Det data som användes i studien bestod av 2501 artiklar från engelska Wikipedia, där varje artikel bestod av en beskrivning av en applikation. Två experiment utfördes för att utvärdera de båda metoderna. Det första experimentet bestod av att mäta till vilken grad de rekommenderade applikationerna tillhörde samma mjukvarukategori som den applikation de rekommenderats som alternativ till. Det andra experimentet bestod av ett antal strukturerade intervjuer, där rekommendationerna för en delmängd av applikationerna utvärderades mer djupgående. Resultaten från experimenten visade endast en liten skillnad mellan de båda metoderna, med en liten fördel till LSI när färre rekommendationer hämtades, och en liten fördel för TF-IDF när fler rekommendationer hämtades. Intervjuerna visade att rekommendationerna från den LSI-baserade implementationen till en högre grad hade liknande funktionalitet som de utvärderade applikationerna, och att rekommendationerna från när TF-IDF användes till en högre grad hade funktionalitet som kompletterade eller förbättrade de utvärderade applikationerna.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:56
Keywords [en]
Recommender system, content based filtering
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-223655OAI: oai:DiVA.org:kth-223655DiVA, id: diva2:1186070
External cooperation
ISOFT Services AB
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Media Technology
Supervisors
Examiners
Available from: 2018-03-02 Created: 2018-02-27 Last updated: 2018-03-02Bibliographically approved

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