Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering
Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/24/11890 |
| _version_ | 1827674238354456576 |
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| author | Silvana Vanesa Aciar Ramon Fabregat Teodor Jové Gabriela Aciar |
| author_facet | Silvana Vanesa Aciar Ramon Fabregat Teodor Jové Gabriela Aciar |
| author_sort | Silvana Vanesa Aciar |
| collection | DOAJ |
| description | Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users. |
| first_indexed | 2024-03-10T04:37:43Z |
| format | Article |
| id | doaj.art-7d341c28ef9e4da1bee6830b262350ca |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:37:43Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7d341c28ef9e4da1bee6830b262350ca2023-11-23T03:39:54ZengMDPI AGApplied Sciences2076-34172021-12-0111241189010.3390/app112411890Enhancing Recommender System with Collaborative Filtering and User Experiences FilteringSilvana Vanesa Aciar0Ramon Fabregat1Teodor Jové2Gabriela Aciar3Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de San Juan, San Juan 5400, ArgentinaBroadband Communications and Distributed Systems, Institut de Informàtica I Aplicacions, Universitat de Girona, 17004 Girona, SpainBroadband Communications and Distributed Systems, Institut de Informàtica I Aplicacions, Universitat de Girona, 17004 Girona, SpainInstituto de Informática, Universidad Nacional de San Juan, San Juan 5400, ArgentinaRecommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.https://www.mdpi.com/2076-3417/11/24/11890recommender systemcollaborative filteringopinion mininguser experience |
| spellingShingle | Silvana Vanesa Aciar Ramon Fabregat Teodor Jové Gabriela Aciar Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering Applied Sciences recommender system collaborative filtering opinion mining user experience |
| title | Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering |
| title_full | Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering |
| title_fullStr | Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering |
| title_full_unstemmed | Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering |
| title_short | Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering |
| title_sort | enhancing recommender system with collaborative filtering and user experiences filtering |
| topic | recommender system collaborative filtering opinion mining user experience |
| url | https://www.mdpi.com/2076-3417/11/24/11890 |
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