A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

Abstract

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

Evolutionary approaches for weight optimization in collaborative filtering-basedrecommender systems

“…Exploration ABC GA ABC GA Employee bee phase Fitness evaluation Scout bee phase Recombination Onlooker bee phase Mutation A number of different studies have employed evolutionary approaches with these considerations in mind [7][8][9][10][11]. The work in [7] presented a novel metric for computing recommendations.…”

“…The formulas used in employee and onlooker bee phases, Eqs. (10) and (11), were adjusted as follows:…”

News recommendation system using machine learning

“…In this research work, ABC is used for the selection of news features. Employed bees, onlooker bees' and scout bees' are the three main groups in the ABC algorithm [11]. Figure 2 shows proposed work flowchart.…”

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

“…The patrilines introduced the distributed approach idea in the search space and hence the parallel execution across multiple units for effective exploration. J u and X u [163] applied ABC in k-Means clustering to overcome the local optimal problems and produce effective clusters. Finally, a recommendation list for the target users is generated using modified collaborative recommendation approach.…”