eprintid: 242
rev_number: 17
eprint_status: archive
userid: 7
dir: disk0/00/00/02/42
datestamp: 2009-10-19 09:44:10
lastmod: 2015-05-29 19:51:11
status_changed: 2009-10-19 09:44:10
type: report
metadata_visibility: show
item_issues_count: 0
creators_name: Apple, Rosemary
creators_name: Cawthorn, Chris
creators_name: Yee Chan, Kwan
creators_name: Lachish, Oded
creators_name: Nonnenmacker, Achim
creators_name: Porter, Mason A.
creators_name: Reboux, Sylvain
creators_name: Hazelwood, Vera
corp_creators: Benjamin Dias
title: Overcoming data sparsity
ispublished: pub
subjects: other
subjects: telecom
studygroups: esgi64
companyname: Unilever UK
full_text_status: public
abstract: 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.
date: 2008
citation:   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]     
document_url: http://miis.maths.ox.ac.uk/miis/242/1/Unilever_OvercomingDataSparsity.pdf