Product recommendation algorithms in the age of omnichannel retailing – An intuitive clustering approach. (January 2018)

• Record Type: Journal Article
• Authors: Balakrishnan, Jaydeep; Cheng, Chun-Hung; Wong, Kam-Fai; Woo, Kwan-Ho
• Is Part Of: Computers & industrial engineering. Volume 115(2018)
• Page Start: 459
• Page End: 470
• Publication Date: 2018-01
• Subjects: Buying patterns -- Product recommendation -- 0–1 matrix -- Co-clustering -- Branch-and-bound -- Duplication; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.12.005
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A co-clustering algorithm for product recommendations for omnichannel retailing is adopted. The execution of the algorithm with duplication strategy is intuitively shown. The managerial perspective of the algorithm is derived. Abstract: In 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.