Recommendation Algorithm Using Clustering-Based UPCSim (CB-UPCSim)

One of the well-known recommendation systems is memory-based collaborative filtering that utilizes similarity metrics. Recently, the similarity metrics have taken into account the user rating and user behavior scores. The user behavior score indicates the user preference in each product type (genre). The added user behavior score to the similarity metric results in more complex computation. To reduce the complex computation, we combined the clustering method and user behavior score-based similarity. The clustering method applies k-means clustering by determination of the number of clusters using the Silhouette Coefficient. Whereas the user behavior score-based similarity utilizes User Profile Correlation-based Similarity (UPCSim). The experimental results with the MovieLens 100k dataset showed a faster computation time of 4.16 s. In addition, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values decreased by 1.88% and 1.46% compared to the baseline algorithm.

[1]  Ville Ollikainen,et al.  A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data , 2015, Knowl. Based Syst..

[2]  Fuguo Zhang,et al.  Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks , 2020, Expert Syst. Appl..

[3]  Ning Zhang,et al.  An improved collaborative filtering method based on similarity , 2018, PloS one.

[4]  Arafat Awajan,et al.  Novel predictive model to improve the accuracy of collaborative filtering recommender systems , 2021, Inf. Syst..

[5]  Chaita Jani,et al.  Implementing & Improvisation of K-means Clustering Algorithm , 2016 .

[6]  Vivekanandan Kalimuthu,et al.  Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization , 2017 .

[7]  Chunming Rong,et al.  Fast algorithms to evaluate collaborative filtering recommender systems , 2016, Knowl. Based Syst..

[8]  Jason J. Jung,et al.  Cognitive Similarity-Based Collaborative Filtering Recommendation System , 2020, Applied Sciences.

[9]  Nikolaos Polatidis,et al.  A multi-level collaborative filtering method that improves recommendations , 2016, Expert Syst. Appl..

[10]  Mahdi Kherad,et al.  Recommendation system using a deep learning and graph analysis approach , 2020, Comput. Intell..

[11]  Hui Tian,et al.  A new user similarity model to improve the accuracy of collaborative filtering , 2014, Knowl. Based Syst..

[12]  Xiaoping Fan,et al.  A Novel Hybrid Similarity Calculation Model , 2017, Sci. Program..

[13]  Zhi-Heng Zhang,et al.  Integrating Triangle and Jaccard similarities for recommendation , 2017, PloS one.

[14]  Eusebio Scornavacca,et al.  Exploring the effects of different Clustering Methods on a News Recommender System , 2021, Expert Syst. Appl..

[15]  Jing Wu,et al.  Recommendation algorithm based on user score probability and project type , 2019, EURASIP J. Wirel. Commun. Netw..

[16]  Teguh Bharata Adji,et al.  User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system , 2021, J. Big Data.

[17]  Sivaramakrishnan Natarajan,et al.  Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method , 2018, Neural Computing and Applications.

[18]  Vimal Pambhar,et al.  ENHANCED K-MEANS CLUSTRING ALGORITHM TO REDUCE TIME COMPLEXITY FOR NUMERIC VALUES , 2014 .

[19]  J. Mayor,et al.  CF4J 2.0: Adapting Collaborative Filtering for Java to new challenges of collaborative filtering based recommender systems , 2020, Knowl. Based Syst..

[20]  Jiang Sheng-yi.  An Enhanced k-means Clustering Algorithm , 2006 .

[21]  Jason J. Jung,et al.  Extending collaborative filtering recommendation using word embedding: A hybrid approach , 2021, Concurr. Comput. Pract. Exp..

[23]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[24]  Mario Ventresca,et al.  Improving neighbor-based collaborative filtering by using a hybrid similarity measurement , 2020, Expert Syst. Appl..

[25]  Iraklis Varlamis,et al.  Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance , 2019, Inf..