The MIIS Eprints Archive

Overcoming data sparsity

Apple, Rosemary and Cawthorn, Chris and Yee Chan, Kwan and Lachish, Oded and Nonnenmacker, Achim and Porter, Mason A. and Reboux, Sylvain and Hazelwood, Vera (2008) Overcoming data sparsity. [Study Group Report]



Unilever is currently designing and testing recommendation algorithms that would make recommendations about products to online customers given the customer ID and the current content of their basket. Unilever collected a large amount of purchasing data that demonstrates that most of the items (around 80%) are purchased infrequently and account for 20% of the data while frequently purchased items account for 80% of the data. Therefore, the data is sparse, skewed and demonstrates a long tail. Attempts to incorporate the data from the long tail, so far have proved difficult and current Unilever recommendation systems do not incorporate the information about infrequently purchased items. At the same time, these items are more indicative of customers' preferences and Unilever would like to make recommendations from/about these items, i.e. give a rank ordering of available products in real time.

Study Group suggested to use the approach of bipartite networks to construct a similarity matrix that would allow the recommendation scores for different products to be computed. Given a current basket and a customer ID, this approach gives recommendation scores for each available item and recommends the item with the highest score that is not already in the basket. The similarity matrix can be computed offline, while recommendation score calculations can be performed live. This report contains the summary of Study Group findings together with the insights into properties of the similarity matrix and other related issues, such as recommendation for the data collection.

Item Type:Study Group Report
Problem Sectors:None/Other
Information and communication technology
Study Groups:European Study Group with Industry > ESGI 64 (Heriot-Watt, UK, Apr 7-11, 2008)
Company Name:Unilever UK
ID Code:242
Deposited By: Dr Kamel Bentahar
Deposited On:19 Oct 2009 09:44
Last Modified:29 May 2015 19:51

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