A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques
Multi-criteria collaborative filtering (MC-CF) presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects of items. However, scalability and sparsity are two main problems in MC-CF which this paper attempts to solve them using dimensionality re...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
Springer
2015
|
| Subjects: |
| _version_ | 1796860204991643648 |
|---|---|
| author | Nilashi, Mehrbakhsh Ibrahim, Othman Ithnin, Norafida Zakaria, Rozana |
| author_facet | Nilashi, Mehrbakhsh Ibrahim, Othman Ithnin, Norafida Zakaria, Rozana |
| author_sort | Nilashi, Mehrbakhsh |
| collection | ePrints |
| description | Multi-criteria collaborative filtering (MC-CF) presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects of items. However, scalability and sparsity are two main problems in MC-CF which this paper attempts to solve them using dimensionality reduction and Neuro-Fuzzy techniques. Considering the user behavior about items’ features which is frequently vague, imprecise and subjective, we solve the sparsity problem using Neuro-Fuzzy technique. For the scalability problem, higher order singular value decomposition along with supervised learning (classification) methods is used. Thus, the objective of this paper is to propose a new recommendation model to improve the recommendation quality and predictive accuracy of MC-CF and solve the scalability and alleviate the sparsity problems in the MC-CF. The experimental results of applying these approaches on Yahoo!Movies and TripAdvisor datasets with several comparisons are presented to show the enhancement of MC-CF recommendation quality and predictive accuracy. The experimental results demonstrate that SVM dominates the K-NN and FBNN in improving the MC-CF predictive accuracy evaluated by most broadly popular measurement metrics, F1 and mean absolute error. In addition, the experimental results also demonstrate that the combination of Neuro-Fuzzy and dimensionality reduction techniques remarkably improves the recommendation quality and predictive accuracy of MC-CF in relation to the previous recommendation techniques based on multi-criteria ratings. |
| first_indexed | 2024-03-05T19:38:28Z |
| format | Article |
| id | utm.eprints-55703 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:38:28Z |
| publishDate | 2015 |
| publisher | Springer |
| record_format | dspace |
| spelling | utm.eprints-557032017-02-15T01:49:58Z http://eprints.utm.my/55703/ A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques Nilashi, Mehrbakhsh Ibrahim, Othman Ithnin, Norafida Zakaria, Rozana QA75 Electronic computers. Computer science Multi-criteria collaborative filtering (MC-CF) presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects of items. However, scalability and sparsity are two main problems in MC-CF which this paper attempts to solve them using dimensionality reduction and Neuro-Fuzzy techniques. Considering the user behavior about items’ features which is frequently vague, imprecise and subjective, we solve the sparsity problem using Neuro-Fuzzy technique. For the scalability problem, higher order singular value decomposition along with supervised learning (classification) methods is used. Thus, the objective of this paper is to propose a new recommendation model to improve the recommendation quality and predictive accuracy of MC-CF and solve the scalability and alleviate the sparsity problems in the MC-CF. The experimental results of applying these approaches on Yahoo!Movies and TripAdvisor datasets with several comparisons are presented to show the enhancement of MC-CF recommendation quality and predictive accuracy. The experimental results demonstrate that SVM dominates the K-NN and FBNN in improving the MC-CF predictive accuracy evaluated by most broadly popular measurement metrics, F1 and mean absolute error. In addition, the experimental results also demonstrate that the combination of Neuro-Fuzzy and dimensionality reduction techniques remarkably improves the recommendation quality and predictive accuracy of MC-CF in relation to the previous recommendation techniques based on multi-criteria ratings. Springer 2015-10 Article PeerReviewed Nilashi, Mehrbakhsh and Ibrahim, Othman and Ithnin, Norafida and Zakaria, Rozana (2015) A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques. Soft Computing, 19 (11). pp. 3173-3207. ISSN 1432-7643 http://dx.doi.org/10.1007/s00500-014-1475-6 DOI:10.1007/s00500-014-1475-6 |
| spellingShingle | QA75 Electronic computers. Computer science Nilashi, Mehrbakhsh Ibrahim, Othman Ithnin, Norafida Zakaria, Rozana A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques |
| title | A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques |
| title_full | A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques |
| title_fullStr | A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques |
| title_full_unstemmed | A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques |
| title_short | A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques |
| title_sort | multi criteria recommendation system using dimensionality reduction and neuro fuzzy techniques |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT nilashimehrbakhsh amulticriteriarecommendationsystemusingdimensionalityreductionandneurofuzzytechniques AT ibrahimothman amulticriteriarecommendationsystemusingdimensionalityreductionandneurofuzzytechniques AT ithninnorafida amulticriteriarecommendationsystemusingdimensionalityreductionandneurofuzzytechniques AT zakariarozana amulticriteriarecommendationsystemusingdimensionalityreductionandneurofuzzytechniques AT nilashimehrbakhsh multicriteriarecommendationsystemusingdimensionalityreductionandneurofuzzytechniques AT ibrahimothman multicriteriarecommendationsystemusingdimensionalityreductionandneurofuzzytechniques AT ithninnorafida multicriteriarecommendationsystemusingdimensionalityreductionandneurofuzzytechniques AT zakariarozana multicriteriarecommendationsystemusingdimensionalityreductionandneurofuzzytechniques |