Collaborative Filtering Recommender Systems Based on k-means Multi-clustering

Kużelewska, Urszula

doi:10.1007/978-3-319-91446-6_30

Cited by 6 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e recommendation performance is evaluated by precision, recall, novelty, and diversity. Moreover, the results of the top personal recommendations are compared with a set of baseline algorithms, including the K-nearest neighbors algorithm (K-NN) [53], K-means clustering algorithm [54], co-clustering [55], nonnegative matrix factorization (NMF) [56], and singular value decomposition (SVD) algorithm [57].…”

Section: Personal Recommendations Evaluation Designmentioning

confidence: 99%

A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Huang

Stakhiyevich

2021

Complexity

View full text Add to dashboard Cite

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.

show abstract

Section: Personal Recommendations Evaluation Designmentioning

confidence: 99%

A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Huang

Stakhiyevich

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…By reducing the size of the data, the computational speed will be improved. In [15], the clustering algorithm was combined with a Collaborative Filtering algorithm to improve the recommendation's quality.…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Serendipity Identification Using Distance-Based Approach

Hartanto

Setiawan

Adji

2021

IJITEE

View full text Add to dashboard Cite

The recommendation system is a method for helping consumers to find products that fit their preferences. However, recommendations that are merely based on user preference are no longer satisfactory. Consumers expect recommendations that are novel, unexpected, and relevant. It requires the development of a serendipity recommendation system that matches the serendipity data character. However, there are still debates among researchers about the available common definition of serendipity. Therefore, our study proposes a work to identify serendipity data's character by directly using serendipity data ground truth from the famous Movielens dataset. The serendipity data identification is based on a distance-based approach using collaborative filtering and k-means clustering algorithms. Collaborative filtering is used to calculate the similarity value between data, while k-means is used to cluster the collaborative filtering data. The resulting clusters are used to determine the position of the serendipity cluster. The result of this study shows that the average distance between the recommended movie cluster and the serendipity movie cluster is 0.85 units, which is neither the closest cluster nor the farthest cluster from the recommended movie cluster.

show abstract

Effect of Dataset Size on Efficiency of Collaborative Filtering Recommender Systems with Multi-clustering as a Neighbourhood Identification Strategy

Kużelewska

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Collaborative Filtering Recommender Systems Based on k-means Multi-clustering

Cited by 6 publications

References 11 publications

A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Serendipity Identification Using Distance-Based Approach

Effect of Dataset Size on Efficiency of Collaborative Filtering Recommender Systems with Multi-clustering as a Neighbourhood Identification Strategy

Contact Info

Product

Resources

About