Using Collaborative Filtering Algorithm to Estimate the Predictive Power of a Functional Requirement

Collaborative filtering (CF) algorithm uses the preferences expressed by previous users of items being studied and is widely applied to build recommender systems. A collaborative filter predicts items that a user will like based on the vote similar users gave to that item. In this study, we use CF t...

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Main Authors: Hidalgo, Reynald Jay F, Fernandez, Proceso L, Jr
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Published: Archīum Ateneo 2020
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Online Access:https://archium.ateneo.edu/discs-faculty-pubs/276
https://dl.acm.org/doi/10.1145/3377571.3377605
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.discs-faculty-pubs-12792022-03-18T07:15:32Z Using Collaborative Filtering Algorithm to Estimate the Predictive Power of a Functional Requirement Hidalgo, Reynald Jay F Fernandez, Proceso L, Jr Collaborative filtering (CF) algorithm uses the preferences expressed by previous users of items being studied and is widely applied to build recommender systems. A collaborative filter predicts items that a user will like based on the vote similar users gave to that item. In this study, we use CF to estimate how much the knowledge of the presence or absence of one software feature can contribute to the correct prediction of the presence or absence of each of the possible remaining features. Completed software project documentations from the Master in Information Technology programs of selected Northern Luzon higher education institutions were first collected. An analysis of these documents revealed 26 unique software features and yielded a binary matrix indicating the presence or absence of a feature in a specific project. Leave-one-out cross-validation was performed to estimate the predictive power of each element of a given holdout vector, using the 26x26 cosine similarity matrix generated from the remaining vectors. The results show that, on average, knowing correctly the presence or absence of only 1 feature can predict with an accuracy of about 58% the presence or absence of the remaining features. This is 8% better than that of a naïve 50-50 random binary guessing algorithm, and somehow indicates the amount of information contributed by one feature value under the CF algorithm. 2020-01-01T08:00:00Z text https://archium.ateneo.edu/discs-faculty-pubs/276 https://dl.acm.org/doi/10.1145/3377571.3377605 Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo Information Systems Recommender Systems Computer Sciences Databases and Information Systems
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Information Systems
Recommender Systems
Computer Sciences
Databases and Information Systems
spellingShingle Information Systems
Recommender Systems
Computer Sciences
Databases and Information Systems
Hidalgo, Reynald Jay F
Fernandez, Proceso L, Jr
Using Collaborative Filtering Algorithm to Estimate the Predictive Power of a Functional Requirement
description Collaborative filtering (CF) algorithm uses the preferences expressed by previous users of items being studied and is widely applied to build recommender systems. A collaborative filter predicts items that a user will like based on the vote similar users gave to that item. In this study, we use CF to estimate how much the knowledge of the presence or absence of one software feature can contribute to the correct prediction of the presence or absence of each of the possible remaining features. Completed software project documentations from the Master in Information Technology programs of selected Northern Luzon higher education institutions were first collected. An analysis of these documents revealed 26 unique software features and yielded a binary matrix indicating the presence or absence of a feature in a specific project. Leave-one-out cross-validation was performed to estimate the predictive power of each element of a given holdout vector, using the 26x26 cosine similarity matrix generated from the remaining vectors. The results show that, on average, knowing correctly the presence or absence of only 1 feature can predict with an accuracy of about 58% the presence or absence of the remaining features. This is 8% better than that of a naïve 50-50 random binary guessing algorithm, and somehow indicates the amount of information contributed by one feature value under the CF algorithm.
format text
author Hidalgo, Reynald Jay F
Fernandez, Proceso L, Jr
author_facet Hidalgo, Reynald Jay F
Fernandez, Proceso L, Jr
author_sort Hidalgo, Reynald Jay F
title Using Collaborative Filtering Algorithm to Estimate the Predictive Power of a Functional Requirement
title_short Using Collaborative Filtering Algorithm to Estimate the Predictive Power of a Functional Requirement
title_full Using Collaborative Filtering Algorithm to Estimate the Predictive Power of a Functional Requirement
title_fullStr Using Collaborative Filtering Algorithm to Estimate the Predictive Power of a Functional Requirement
title_full_unstemmed Using Collaborative Filtering Algorithm to Estimate the Predictive Power of a Functional Requirement
title_sort using collaborative filtering algorithm to estimate the predictive power of a functional requirement
publisher Archīum Ateneo
publishDate 2020
url https://archium.ateneo.edu/discs-faculty-pubs/276
https://dl.acm.org/doi/10.1145/3377571.3377605
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