Balakrishnan, JaydeepCheng, Chun-HungWong, Kam-FaiWoo, Kwanho2018-10-252018-10-252018-01Balakrishnan, J., Cheng, C-H., Wong, K-F., & Woo, K. (2018). Product recommendation algorithms in the age of omnichannel retailing - An intuitive clustering approach. "Computers & Industrial Engineering", 115, 459-470. https://doi.org/10.1016/j.cie.2017.12.005http://hdl.handle.net/1880/108921Article deposited according to publisher policy posted on SHERPA/ROMEO, 12/05/2017In today’s omnichannel retailing world, product recommendations have become important in retailer strategy. Using big data to recommend complementary products can help improve customer service and thereby increase profitability. A common implementation for studying buying behaviour of customers uses a 0–1 matrix linking the customers to the products they have purchased in the past. However, this raw matrix does not automatically reveal buying patterns. Further processing of this matrix is necessary to find valuable information. In this work, we adopt an intuitive co-clustering algorithm for locating useful patterns in the matrix. The advantage of duplication of products in the clustering process will be shown. A further advantage of the algorithm from a managerial perspective is that it is intuitive rather than a black box type and thus may increase the chances of it being actually adopted.enUnless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Buying patternsProduct recommendation0-1 matrixCo-clusteringBranch-and-boundDuplicationjournal article10.1016/j.cie.2017.12.00510.11575/PRISM/34195