Abstract
A recommender system becomes more and more popular in e-commerce. Usually prediction results cannot satisfy users’ requirements fully, and sometimes it even contains totally irrelevant items. To reflect users’ newest preference and increase the quality of recommendation, a fast interactive item-based collaborative filtering algorithm is proposed. Firstly, we propose an item-based collaborative filtering algorithm with less time and space complexity. Then we introduce interactive iterations to reflect users’ up-to-date preference and increase users’ satisfaction. The experiments show that our fast interactive item-based CF algorithm has better recall and precision than traditional item-based CF algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Park, D.H., Kim, H.K., Choi, I.Y., Kim, J.K.: A literature review and classification of recommender systems research. Expert Syst. Appl. 39(11), 10059–10072 (2012)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)
Gemmell, J., Schimoler, T., Ramezani, M., Christiansen, L., Mobasher, B.: Improving folkrank with item-based collaborative filtering. In: Recommender Systems & the Social Web (2009)
Umyarov, A., Tuzhilin, A.: Improving collaborative filtering recommendations using external data. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 618–627. IEEE (2008)
Xiao, J., Luo, M., Chen, J.-M., Li, J.-J.: An item based collaborative filtering system combined with genetic algorithms using rating behavior. In: Huang, D.-S., Han, K. (eds.) ICIC 2015. LNCS, vol. 9227, pp. 453–460. Springer, Cham (2015). doi:10.1007/978-3-319-22053-6_48
Gong, S., Ye, H.: An item based collaborative filtering using bp neural networks prediction. In: International Conference on Industrial and Information Systems, IIS 2009, pp. 146–148. IEEE (2009)
Ben Shimon, D., Rokach, L., Shapira, B., Shani, G.: Fast item-based collaborative filtering. In: Proceedings of the International Conference on Agents and Artificial Intelligence-Volume 2, pp. 457–463. SCITEPRESS-Science and Technology Publications, Lda (2015)
Pirasteh, P., Jung, J.J., Hwang, D.: Item-based collaborative filtering with attribute correlation: a case study on movie recommendation. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014. LNCS, vol. 8398, pp. 245–252. Springer, Cham (2014). doi:10.1007/978-3-319-05458-2_26
Zhao, Z.-L., Wang, C.-D., Wan, Y.-Y., Huang, Z.-W., Lai, J.-H.: Pipeline item-based collaborative filtering based on MapReduce. In: 2015 IEEE Fifth International Conference on Big Data and Cloud Computing (BDCloud), pp. 9–14. IEEE (2015)
Lu, F., Hong, L., Changfeng, L.: The improvement and implementation of distributed item-based collaborative filtering algorithm on Hadoop. In: 2015 34th Chinese Control Conference (CCC), pp. 9078–9083. IEEE (2015)
Latha, R., Nadarajan, R.: User relevance for item-based collaborative filtering. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) CISIM 2013. LNCS, vol. 8104, pp. 337–347. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40925-7_31
Cadegnani, S., Guerra, F., Ilarri, S., del Carmen Rodríguez-Hernández, M., Trillo-Lado, R., Velegrakis, Y.: Recommending web pages using item-based collaborative filtering approaches. In: Cardoso, J., Guerra, F., Houben, G.J., Pinto, A., Velegrakis, Y. (eds.) KEYSTONE 2015. LNCS, vol. 9398, pp. 17–29. Springer, Cham (2015). doi:10.1007/978-3-319-27932-9_2
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. Paper presented at the Proceedings of the 1993 ACM SIGMOD international conference on Management of data, Washington, D.C., USA (1993)
Agrawal, R., Srikant, R.: Algorithms for mining association rules in large databases. In: Proceedings of the 20th VLDB Conference Santiago, Chile, pp. 141–182 (1994)
Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. ACM Trans. Interact. Intell. Syst. (TiiS) 5(4), 19 (2016)
Wen, J., Zhou, W.: An improved item-based collaborative filtering algorithm based on clustering method. J. Comput. Inf. Syst. 8(2), 571–578 (2012)
Birtolo, C., Ronca, D., Armenise, R.: Improving accuracy of recommendation system by means of Item-based Fuzzy Clustering Collaborative Filtering. In: 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 100–106. IEEE (2011)
Hui, S., Pengyu, L., Kai, Z.: Improving item-based collaborative filtering recommendation system with tag. In: 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), pp. 2142–2145. IEEE (2011)
Mu, X., Chen, Y., Zhang, L.: An improved similarity algorithm based on stability degree for item-based collaborative filtering. In: 2010 International Conference on Computer Design and Applications (ICCDA), pp. V3-494–V493-498. IEEE (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ji, Z., Zhang, Z., Zhou, C., Wang, H. (2017). A Fast Interactive Item-Based Collaborative Filtering Algorithm. In: Du, D., Li, L., Zhu, E., He, K. (eds) Theoretical Computer Science. NCTCS 2017. Communications in Computer and Information Science, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-10-6893-5_19
Download citation
DOI: https://doi.org/10.1007/978-981-10-6893-5_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6892-8
Online ISBN: 978-981-10-6893-5
eBook Packages: Computer ScienceComputer Science (R0)