Evolutionary approaches for weight optimization in collaborative filtering-based recommender systems
Evolutionary approaches for weight optimization in collaborative filtering-based recommender systems
Collaborative filtering is one of the widely adopted approaches in recommender systems used for e-commerceapplications, stating that users having similar tastes will have similar preferences in the future. The literature presentsa number of similarity metrics such as the extended Jaccard coefficient to quantify these preference similarities. Thispaper aims to improve prediction accuracy by optimizing the similarity values computed using these metrics by adoptingtwo biologically inspired approaches, namely artificial bee colony and genetic algorithms, with a bottom-up approach,suggesting that any improvement on a single-user basis will reflect on the overall prediction accuracy. Detailed statisticalanalysis was performed using the t-test, analysis of variance, and McNemar’s test to see whether there were performancedifferences. The results show that statistically significant differences exist with high confidence levels.
___
- [1] Simon D. Social media equals social customer: managing customer experience in the age of social media. iUniverse,
2013.
- [2] Resnick P, Varian HR. Recommender systems. Communications of the ACM 1997; 40: 56-58.
- [3] Lops P, De Gemmis M, Semeraro G. Content-based recommender systems: state of the art and trends. In: Ricci F,
Rokach L, Shapira B, Kantor PB (editors). Recommender Systems Handbook. Berlin, Germany: Springer, 2011,
pp. 73-105.
- [4] Schafer JB, Frankowski D, Herlocker J, Sen S. Collaborative filtering recommender systems. In: Brusilovsky P,
Kobsa A, Nejdl W (editors). The Adaptive Web. Berlin, Germany: Springer-Verlag, 2007, pp. 291-324.
- [5] Balabanovic M, Shoham Y. Combining content-based and collaborative recommendation. Communications of the
ACM 1997; 40: 66-72.
- [6] Eiben AE, Smith JE. Introduction to Evolutionary Computing. Berlin, Germany: Springer-Verlag, 2003.
- [7] Bobadilla J, Ortega F, Hernando A, Alcalá J. Improving collaborative filtering recommender system results and
performance using genetic algorithms. Knowledge-Based Systems 2011; 24: 1310-1316.
- [8] Hsu CC, Chen HC, Huang KK, Huang YM. A personalized auxiliary material recommendation system based
on learning style on facebook applying an artificial bee colony algorithm. Computers and Mathematics with
Applications 2012; 64: 1506-1513.
- [9] Anand D. Feature extraction for collaborative filtering: a genetic programming approach. International Journal
Computer Science Issues 2012; 9 (5): 348.
- [10] Velez-Langs O, De Antonio A. Learning user’s characteristics in collaborative filtering through genetic algorithms:
some new results. Advance Trends in Soft Computing 2014; 312: 309-326.
- [11] Ju C, Xu C. A new collaborative recommendation approach based on users clustering using artificial bee colony
algorithm. Scientific World Journal 2013; 2013: 869658.
- [12] Akbari F, Tajfar A, Nejad A. Graph-based friend recommendation in social networks using artificial bee colony. In:
IEEE Conference on Dependable, Autonomic and Secure Computing; 2013. pp. 464-468.
- [13] Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In:
Fourteenth Conference on Uncertainty in Artificial Intelligence; San Francisco, CA, USA; 1998. pp. 43-52.
- [14] Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. Grouplens: An open architecture for collaborative filtering
of netnews. In: 1994 ACM Conference on Computer Supported Cooperative Work; Chapel Hill, NC, USA; 1994.
pp. 175-186.
- [15] Herlocker JL, Konstan JA, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering.
In: 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval;
New York, NY, USA; 2017. pp. 227-234.
- [16] Golbeck J, Hendler J. Filmtrust: Movie recommendations using trust in web-based social networks. In: IEEE
Consumer Communications and Networking Conference; Las Vegas, NV, USA; 2006. pp. 282-286.
- [17] Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: 10th
International Conference on WWW; Nanjing, China; 2012. pp. 285-295.
- [18] Linden G, Smith B, York J. Amazon.com recommendations: item-to-item collaborative filtering. Internet Computing IEEE 2003; 7 (1): 76-80.
- [19] Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR et al. Grouplens: Applying collaborative filtering to
usenet news. Communications of the ACM 1997; 40: 77-87.
- [20] Salehi M, Pourzaferani M, Razavi SA. Hybrid attribute-based recommender system for learning material using
genetic algorithm and a multidimensional information model. Egyptian Informatics Journal 2013; 14: 67-78.
- [21] Ar Y, Bostanci E. A genetic algorithm solution to the collaborative filtering problem. Expert Systems with
Applications 2016; 61: 122-128.
- [22] Karaboga D, Gorkemli B. A quick artificial bee colony (qABC) algorithm and its performance on optimization
problems. Applied Soft Computing 2014; 23: 227-238.
- [23] Draa A, Bouaziz A. An artificial bee colony algorithm for image contrast enhancement. Swarm and Evolutionary
Computation 2014; 16: 69-84.
- [24] Agrawal S, Sahu O. Artificial bee colony algorithm to design two-channel quadrature mirror filter banks. Swarm
and Evolutionary Computation 2015; 21: 24-31.
- [25] Yigit S, Tugrul B, Celebi FV. A complete cad model for type-I quantum cascade lasers with the use of artificial bee
colony algorithm. Journal of Artificial Intelligence 2012; 5: 76-84.
- [26] Subotic M, Mialn T, Stanarevic N. Different approaches in parallelization of the artificial bee colony algorithm.
International Journal of Mathematical Models and Methods in Applied Sciences 2011; 5: 755-762.
- [27] Zhu G, Kwong S. Gbest-guided ABC algorithm for numerical function optimization. Applied Mathematics and
Computation 2010; 217: 3166-3173.
- [28] Gao WF, Liu SY. A modified artificial bee colony algorithm. Computers and Operations Research 2012; 39: 687-697.
- [29] Banharnsakun A, Achalakul T, Sirinaovakul B. The best-so-far selection in artificial bee colony algorithm. Applied
Soft Computing 2011; 11: 2888-2901.
- [30] Harper FM, Konstan JA. The MovieLens datasets: history and context. ACM Transactions on Interactive Intelligent
Systems 2015; 5 (4): 19:1-19:19.
- [31] Bostanci B, Bostanci E. An evaluation of classification algorithms using McNemar’s test. In: Seventh International
Conference on Bio-Inspired Computing: Theories and Applications, Advances in Intelligent Systems and Computing;
India; 2013. pp. 15-26.
- [32] Gantner Z, Rendle S, Freudenthaler C, Schmidt-Thieme L. MyMediaLite: A free recommender system library. In:
5th ACM Conference on Recommender Systems; Chicago, IL, USA; 2011. pp. 305-308.