New contextual collaborative filtering system with application to personalized healthy nutrition education
Nowadays, the Internet is becoming a platform of choice where the number of users and items grows dramatically making recommender systems (RS) the most required and widespread technology. This paper deals with context aware collaborative RS and presents a double contribution that consists of a two D...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820303475 |
| _version_ | 1819156803086188544 |
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| author | Hanane Zitouni Souham Meshoul Chaker Mezioud |
| author_facet | Hanane Zitouni Souham Meshoul Chaker Mezioud |
| author_sort | Hanane Zitouni |
| collection | DOAJ |
| description | Nowadays, the Internet is becoming a platform of choice where the number of users and items grows dramatically making recommender systems (RS) the most required and widespread technology. This paper deals with context aware collaborative RS and presents a double contribution that consists of a two Dimensions Contextual Collaborative Recommender System (2DCCRS) and a related application. Our first contribution proposes a new framework for collaborative context aware RS that relies on two key ideas. The first one suggests splitting the context into two parts namely internal and external contexts in order to deal with both internal and external context attributes in different and more appropriate manners. This allows addressing the complexity of the context model in a more effective way. In the second idea, we introduce two concepts; namely the “Stakeholders” and “Aggregation” to effectively alleviate the problems of new user and new item. 2DCCRS is based on a multi-layer architecture. Its highest layer relies on a pre-filtering algorithm that deals with the cold start system problem, and is mainly based on the similarity between the user profile and the items features. The middle layer is based on a collaborative filtering algorithm that takes into account the users’ preferences, interests and priorities; while the deepest layer, which is considered the most relevant in our multilayer architecture, focuses on a post-filtering algorithm in which the recommendations are much more adapted to the user environment. In Our second contribution, we present a case study of 2DCCRS in order to demonstrate the usefulness and effectiveness of the proposed approach. Indeed, we propose a personalized Healthy and Tasty application (H&T) that generates items based on 2DCCRS framework to guide the user toward the healthy and tasty meals that best meet his needs. The obtained results are very promising and show the effectiveness of our proposal. |
| first_indexed | 2024-12-22T15:58:40Z |
| format | Article |
| id | doaj.art-2607ca796419466984f0b973d5a632db |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-22T15:58:40Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-2607ca796419466984f0b973d5a632db2022-12-21T18:20:43ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-04-0134411241137New contextual collaborative filtering system with application to personalized healthy nutrition educationHanane Zitouni0Souham Meshoul1Chaker Mezioud2University of Constantine 2 Abdelhamid Mehri, Constantine, Algeria; Corresponding author at: Ali Menjli, Constantine 2: Abdlhamid Mehri University, Constantine 25000, Algeria.Princess Nora Bint Abdulrahman University, IT Department, CCIS Research Center, Saudi ArabiaUniversity of Constantine 2 Abdelhamid Mehri, Constantine, AlgeriaNowadays, the Internet is becoming a platform of choice where the number of users and items grows dramatically making recommender systems (RS) the most required and widespread technology. This paper deals with context aware collaborative RS and presents a double contribution that consists of a two Dimensions Contextual Collaborative Recommender System (2DCCRS) and a related application. Our first contribution proposes a new framework for collaborative context aware RS that relies on two key ideas. The first one suggests splitting the context into two parts namely internal and external contexts in order to deal with both internal and external context attributes in different and more appropriate manners. This allows addressing the complexity of the context model in a more effective way. In the second idea, we introduce two concepts; namely the “Stakeholders” and “Aggregation” to effectively alleviate the problems of new user and new item. 2DCCRS is based on a multi-layer architecture. Its highest layer relies on a pre-filtering algorithm that deals with the cold start system problem, and is mainly based on the similarity between the user profile and the items features. The middle layer is based on a collaborative filtering algorithm that takes into account the users’ preferences, interests and priorities; while the deepest layer, which is considered the most relevant in our multilayer architecture, focuses on a post-filtering algorithm in which the recommendations are much more adapted to the user environment. In Our second contribution, we present a case study of 2DCCRS in order to demonstrate the usefulness and effectiveness of the proposed approach. Indeed, we propose a personalized Healthy and Tasty application (H&T) that generates items based on 2DCCRS framework to guide the user toward the healthy and tasty meals that best meet his needs. The obtained results are very promising and show the effectiveness of our proposal.http://www.sciencedirect.com/science/article/pii/S1319157820303475Context aware recommender systemsCollaborative filteringInternal contextExternal contextStakeholderAggregation |
| spellingShingle | Hanane Zitouni Souham Meshoul Chaker Mezioud New contextual collaborative filtering system with application to personalized healthy nutrition education Journal of King Saud University: Computer and Information Sciences Context aware recommender systems Collaborative filtering Internal context External context Stakeholder Aggregation |
| title | New contextual collaborative filtering system with application to personalized healthy nutrition education |
| title_full | New contextual collaborative filtering system with application to personalized healthy nutrition education |
| title_fullStr | New contextual collaborative filtering system with application to personalized healthy nutrition education |
| title_full_unstemmed | New contextual collaborative filtering system with application to personalized healthy nutrition education |
| title_short | New contextual collaborative filtering system with application to personalized healthy nutrition education |
| title_sort | new contextual collaborative filtering system with application to personalized healthy nutrition education |
| topic | Context aware recommender systems Collaborative filtering Internal context External context Stakeholder Aggregation |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820303475 |
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