Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method

By D. Adeniyi, Z. Wei, Y. Yongquan. 2016

410 citations recorded by the source corpus.

Abstract The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS) reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN) classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/f8cfdc3cbda08caf7157de27e230b5129e56e101

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bo Li
2. Dawn Song
3. Zhaochun Ren
4. Liqiang Nie
5. Costas J. Spanos
6. Tian Gan
7. Ce Zhang
8. Changfeng Sun

In Information Systems

1. Zhaochun Ren
2. Dawn Song
3. Bo Li
4. Liqiang Nie
5. Tian Gan
6. Ce Zhang
7. Changfeng Sun
8. Han Liu

Citations in the corpus

• LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation (2020, methodology): “, matrix factorization based methods [19, 31, 39] and k-nearest neighbor based methods [1, 26], have been proposed by computing the similarity”
• Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms (2019, methodology): “Specifically, we study K -nearest neighbors ( K NN) classifiers [19], a simple yet popular supervised learning method used in image recognition [25], recommendation systems [6], healthcare [38], etc.”
• A systematic review and research perspective on recommender systems (2022, methodology): “K-nearest K-nearest neighbours (KNN) [19, 37, 44] technique provides faster computation time, simplicity and ease of interpretation.”