Search. Read. Cite.

Easy to search. Easy to read. Easy to cite with credible sources.

Research Article
Recommender System Based on Collaborative Behavior of Ants

P. Bedi, R. Sharma and H. Kaur

Journal of Artificial Intelligence, 2009, 2(2), 40-55.


This study uses collaborative filtering approach and proposes an Ant Recommender System (ARS) based on collaborative behavior of ants for generating Top-N recommendations. Present proposed system ARS works in two phases. In the first phase, opinions from users collected in the form of user-item rating matrix are clustered offline using ant based clustering algorithm into predetermined number of clusters and stored in the database for future recommendations. In the second phase, the recommendations are generated online for the active user. The pheromone updating strategy of ants is combined with similarity measure for choosing the clusters with good quality ratings. This helps in improving the quality of recommendations for the active user. The performance of ARS is evaluated using Jester dataset available on the website of University of California, Berkeley and compared with traditional collaborative filtering based recommender system.

ASCI-ID: 33-15

Cited References Fulltext

Similar Articles

A Novel Metric for Comparing the Intelligence of Two Swarm Multiagent Systems

Journal of Artificial Intelligence, 2016, 9(1-3), 39-44.

Cited By

Agent Based Information Retrieval System Using Information Scent

Journal of Artificial Intelligence, 2010, 3(4), 220. DOI: 10.3923/jai.2010.220.238

Improved Win-Win Quiescent Point Algorithm: A Recommender System Approach

Journal of Applied Sciences, 2010, 10(23), 3084. DOI: 10.3923/jas.2010.3084.3090

Interest Based Recommendations with Argumentation

Journal of Artificial Intelligence, 2011, 4(2), 119. DOI: 10.3923/jai.2011.119.142

Hybrid personalized recommender system using centering-bunching based clustering algorithm

Expert Systems with Applications, 2011, (), . DOI: 10.1016/j.eswa.2011.08.020

A survey on context-aware recommender systems based on computational intelligence techniques

Computing, 2015, 97(7), 667. DOI: 10.1007/s00607-015-0448-7

A Study of Recent Recommender System Techniques

International Journal of Knowledge and Systems Science, 2019, 10(2), 13. DOI: 10.4018/IJKSS.2019040102

Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system

Neural Computing and Applications, 2021, 33(12), 7145. DOI: 10.1007/s00521-020-05482-6

FIRMACA-Fuzzy intelligent recommendation model using ant clustering algorithm for social networking

SN Applied Sciences, 2020, 2(10), . DOI: 10.1007/s42452-020-03486-4

Affinity Propagation-Based Hybrid Personalized Recommender System

Complexity, 2022, 2022(), 1. DOI: 10.1155/2022/6958596