This article presents a scalable and optimized recommender system for e-commerce web sites to maintain a better customer relationship management and survive among its competitors. The proposed system analyses the clickstream data obtained from an ecommerce site and predicts the preference level of the customer for the products clicked but not purchased using efficient classifiers such as decision trees, artificial neural networks and extended trees. Collaborative filtering technique is used to recommend products in which similarity measures are used along with efficient rough set leader clustering algorithm which helps in making accurate and fast recommendations. To determine the effectiveness of the proposed approach, an experimental evaluation has been done which clearly depicts the better performance of the system as compared to conventional approaches.
NakamuraA. and AbeN., Collaborative filtering using weighted majority prediction algorithms, In ICML98 (1998), 395–403.
2.
CarmonaC.J., GallegoS.R., TorresF., BernalE., JesúsM.J.D. and GarcíaS., Web usage mining to improve the design of an e-commerce website: OrOliveSur, Com, Expert Systems with Applications39(12) (2012), 11243–11249.
3.
KellyD. and BelkinN.J., Reading time, scrolling and interaction: Exploring implicit sources of user preferences for relevance feedback, Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2001, pp.408–409.
4.
LiD., LvQ., ShangL. and GuN., Item-based top-N recommendation resilient to aggregated information revelation, Knowledge-Based Systems67 (2014), 290–304.
5.
LiuD.R., LaiC.H. and LeeW.J., A hybrid of sequential rules and collaborative filtering for product recommendation, Information Sciences179(20) (2009), 3505–3519.
6.
AdomaviciusG. and TuzhilinA., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering17 (2005), 734–749.
7.
KimH.N., JiA.T., HaI. and JoG.S., Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation, Electronic Commerce Research and Applications9(1) (2010), 73–83.
8.
BreeseJ.S., HeckermanD. and KadieC., Empirical analysis of predictive algorithms for collaborative filtering, In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., 1998, pp. 43–52.
9.
LeeJ.S. and OlafssonS., Two-way cooperative prediction for collaborative filtering recommendations, Expert Systems with Applications36(3) (2009), 5353–5361.
10.
WeiJ., ShenZ., SundaresanN. and MaK.W.L., Visual cluster exploration of web clickstream data, In Visual Analytics Science and Technology (VAST) IEEE Conference, 2012, pp.3–12.
11.
LangK., Newsweeder: Learning to filter netnews, In Proceedings of the 12th International Conference on Machine Learning, 1995, pp. 331–339.
12.
YuK., SchwaighoferA., TrespV., XuX. and KriegelH.-P., Probabilistic memory-based collaborative filtering, IEEE Transactions on Knowledge and Data Engineering16(1) (2004), 56–69.
13.
BelkinN.J. and CroftW.B., Information filtering and information retrieval: Two sides of the same coin?Communications of the ACM35(12) (1992), 29–38.
14.
ResnickP., IacovouN., SuchakM., BergstromP. and RiedlJ., GroupLens: An open architecture for collaborative filtering of netnews, In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, ACM, 1994, pp. 175–186.
15.
MooneyR.J. and RoyL., Content-based book recommending using learning for text categorization, In Proceedings of the Fifth ACM Conference on Digital LibrariesACM, 2000, pp.195–204.
16.
SahebiS. and BrusilovskyP., Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation, In International Conference on User Modeling, Adaptation, and Personalization2013, Springer Berlin Heidelberg, pp.289–295.
17.
ShardanandU. and MaesP., Social information filtering: Algorithms for automating “word of mouth”, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM Press/Addison-Wesley Publishing Co., 1995, pp. 210–217
.
18.
ParkY.J. and ChangK.N., Individual and group behaviour-based customer profile model for personalized product recommendation, Expert Systems with Applications36(2) (2009), 1932–1939.
