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06901nam a2200301Ia 4500 |
| 001 - CONTROL NUMBER |
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73452 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ft6089 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251124121211.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180829n 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Description conventions |
rda |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9 C37 2016 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Ronald Carlos Jr. and Melsheen Kissob. |
| 245 #0 - TITLE STATEMENT |
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| Title |
Further enhancement of collaborative filtering algorithm to be applied in video game recommendation system |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
2016 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Undergraduate Thesis: (BSCS major in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2016. |
| 336 ## - CONTENT TYPE |
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| Content type code |
. |
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Materials specified |
0 |
| Media type code |
. |
| Media type term |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Materials specified |
0 |
| Carrier type code |
. |
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volume |
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rdacarrier |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real world examples of the operation of industry-strength recommender systems. The design of such recommendation engines depends on the domain and the particular characteristics of the data available. For example movie watchers on Netflix frequently provide ratings on a scale of 1 (disliked) to 5 (liked). Such a data source records the quality of interactions between users and items. Additionally, the system may have access to user-specific and item-specific profile attributes such as demographics and product descriptions respectively. Recommender systems differ in the way they analyze these data sources to develop notions of affinity between users and items which can be used to identify well-matched pairs. This study presents the enhancement of the Collaborative Filtering Algorithm to be applied in Video Game Recommendation System. It will focus on the Item-based approach of the Collaborative Filtering which is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). Upon simulating the Collaborative Filtering Algorithm, the researchers found some problems and room for enhancements for the said algorithm. First, there is no threshold for the number of common users. Second, some similarity values are over emphasized. Third, it uses the active user’s mean rating for undefined predictions, where in the algorithm fails to provide recommendation for new users it has no rating history. This study aims to provide solutions for the problems stated and enhance the algorithm for the production of better recommendations. The researchers have researched and studied different articles and literatures that proves the use Collaborative Filtering Algorithm as the best algorithm for recommendation systems. Throughout the study, the researchers found ways to solve the problems in the algorithm. First, the researchers will set a threshold value for the minimum number of common users in the matrix. With this, filtering of similar and non-similar items can be done earlier. Second, items tagged as non-similar will have their similarity calculations decreased. This is to have their values with the similar items treated equally. Lastly, to use the target’s items mean rating instead of the active user’s mean rating. With these objectives, the researchers were able to enhance the algorithm and improve its recommendation accuracy. At the end of the study, the researchers recommend to apply the algorithm to other applications such as behavioural prediction and subject analysis. Also, other categories can be used for filtering aside from genre, eg. Age, Platform, etc. Some processes can also be simplified in order to speed up the prediction process of the algorithm. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
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| Terms governing access |
5 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
ABSTRACT: The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real world examples of the operation of industry-strength recommender systems. The design of such recommendation engines depends on the domain and the particular characteristics of the data available. For example movie watchers on Netflix frequently provide ratings on a scale of 1 (disliked) to 5 (liked). Such a data source records the quality of interactions between users and items. Additionally, the system may have access to user-specific and item-specific profile attributes such as demographics and product descriptions respectively. Recommender systems differ in the way they analyze these data sources to develop notions of affinity between users and items which can be used to identify well-matched pairs. This study presents the enhancement of the Collaborative Filtering Algorithm to be applied in Video Game Recommendation System. It will focus on the Item-based approach of the Collaborative Filtering which is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). Upon simulating the Collaborative Filtering Algorithm, the researchers found some problems and room for enhancements for the said algorithm. First, there is no threshold for the number of common users. Second, some similarity values are over emphasized. Third, it uses the active user's mean rating for undefined predictions, where in the algorithm fails to provide recommendation for new users it has no rating history. This study aims to provide solutions for the problems stated and enhance the algorithm for the production of better recommendations. The researchers have researched and studied different articles and literatures that proves the use Collaborative Filtering Algorithm as the best algorithm for recommendation systems. Throughout the study, the researchers found ways to solve the problems in the algorithm. First, the researchers will set a threshold value for the minimum number of common users in the matrix. With this, filtering of similar and non-similar items can be done earlier. Second, items tagged as non-similar will have their similarity calculations decreased. This is to have their values with the similar items treated equally. Lastly, to use the target's items mean rating instead of the active user's mean rating. With these objectives, the researchers were able to enhance the algorithm and improve its recommendation accuracy. At the end of the study, the researchers recommend to apply the algorithm to other applications such as behavioural prediction and subject analysis. Also, other categories can be used for filtering aside from genre, eg. Age, Platform, etc. Some processes can also be simplified in order to speed up the prediction process of the algorithm. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE |
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| Terms governing use and reproduction |
5 |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
acadenic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Institution code [OBSOLETE] |
lcc |
| Item type |
Archival materials |
| Source of classification or shelving scheme |
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