Product recommendation algorithms in the age of omnichannel retailing - An intuitive clustering approach

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.

[1]  Kam-Fai Wong,et al.  An improved branch-and-bound clustering approach for data partitioning , 2011, Int. Trans. Oper. Res..

[2]  Naftali Tishby,et al.  Document clustering using word clusters via the information bottleneck method , 2000, SIGIR '00.

[3]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[4]  Chun Hung Cheng.  A branch and bound clustering algorithm , 1995, IEEE Trans. Syst. Man Cybern..

[5]  Suman Datta,et al.  SCARS: A scalable context-aware recommendation system , 2015, Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT).

[6]  Songjie Gong.  A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering , 2010, J. Softw..

[7]  Reinhard Heckel,et al.  Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering , 2016, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[8]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[9]  Michail Vlachos,et al.  Unsupervised Sparse Matrix Co-clustering for Marketing and Sales Intelligence , 2012, PAKDD.

[10]  J. Hartigan.  Direct Clustering of a Data Matrix , 1972 .

[11]  Philip S. Yu,et al.  Co-clustering by block value decomposition , 2005, KDD '05.

[12]  SongJie Gong.  Employing User Attribute and Item Attribute to Enhance the Collaborative Filtering Recommendation , 2009, J. Softw..

[13]  Inderjit S. Dhillon,et al.  Information-theoretic co-clustering , 2003, KDD '03.

[14]  Inderjit S. Dhillon,et al.  Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.

[15]  Seyed Mohammad Seyedhosseini,et al.  Machine-Part Cell Formation Problem Using a Group Based League Championship Algorithm , 2015 .

[16]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[17]  Anastasios Kyrillidis,et al.  Improving Co-Cluster Quality with Application to Product Recommendations , 2014, CIKM.