Name
Abstract | ||
|---|---|---|
Recommender HDMR, a polynomial based algorithm for top-N recommendation is proposed.The proposed method is applied to an industrial data set from apparels domain.The data organization is based on collaborative filtering approach.The modelling part includes the implementation of the HDMR philosophy.The results show that Recommender HDMR works better than state-of-the-art methods. Recommendation is the process of identifying and recommending items that are more likely to be of interest to a user. Recommender systems have been applied in variety of fields including e-commerce web pages to increase the sales through the page by making relevant recommendations to users. In this paper, we pose the problem of recommendation as an interpolation problem, which is not a trivial task due to the high dimensional structure of the data. Therefore, we deal with the issue of high dimension by representing the data with lower dimensions using High Dimensional Model Representation (HDMR) based algorithm. We combine this algorithm with the collaborative filtering philosophy to make recommendations using an analytical structure as the data model based on the purchase history matrix of the customers. The proposed approach is able to make a recommendation score for each item that have not been purchased by a customer which potentiates the power of the classical recommendations. Rather than using benchmark data sets for experimental assessments, we apply the proposed approach to a novel industrial data set obtained from an e-commerce web page from apparels domain to present its potential as a recommendation system. We test the accuracy of our recommender system with several pioneering methods in the literature. The experimental results demonstrate that the proposed approach makes recommendations that are of interest to users and shows better accuracy compared to state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.eswa.2017.03.005 | Expert Syst. Appl. |
Keywords | Field | DocType |
Recommender systems,Purchase history matrix,HDMR,E-commerce | Data mining,Data set,Polynomial,Web page,Computer science,Interpolation,Artificial intelligence,High-dimensional model representation,Recommender system,Collaborative filtering,Algorithm,Data model,Machine learning | Journal |
Volume | Issue | ISSN |
79 | C | 0957-4174 |
Citations | PageRank | References |
1 | 0.35 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Özge Yücel Kasap | 1 | 1 | 0.35 |
| M. Alper Tunga | 2 | 40 | 5.44 |