Using Linked Open Data to Improve Recommending on E-Commerce

In this paper, we present our work in progress on using LOD data to enhance recommending on existing e-commerce sites. We imagine a situation of e-commerce website employing contentbased or hybrid recommendation. Such recommending algorithms need relevant object attributes to produce useful recommendations. However, on some domains, usable attributes may be difficult to fill in manually and yet accessible from LOD cloud. For our pilot study, we selected the domain of secondhand bookshops, where recommending is extraordinary difficult because of high ratio of objects/users, lack of significant attributes and small number of the same items in stock. Those difficulties prevents us from successfully apply both collaborative and common content based recommenders. We have queried Czech language mutation of DBPedia in order to receive additional information about objects (books) and use them as Boolean attributes for hybrid matrix factorization method. Our approach is general and can be applied on other domains as well. Proposed methods were successfully tested in an off-line experiment; however we needed to cope with several technical difficulties and obstructions described in the paper, which may hinder widespread of such approaches.