NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

Using Linked Data to Build Open, Collaborative Recommender Systems *
Benjamin Heitmann, Conor Hayes
Digital Enterprise Research Institute, National University of Ireland, Galway
benjamin.heitmann@deri.org, conor.hayes@deri.org
Abstract
While recommender systems can greatly enhance the
user experience, the entry barriers in terms of data
acquisition are very high, making it hard for new
service providers to compete with existing
recommendation services. We propose to build open
recommender systems, which can utilise Linked Data to
mitigate the new-user, new-item, and sparsity problems
of collaborative recommender systems. To demonstrate
the validity of our approach, we augment the data from
a closed collaborative music recommender system with
Linked Data, and significantly improve its precision
and recall.
1. Problem statement
Most real-world recommender systems employ
collaborative filtering [1], which aggregates user ratings
for items and uses statistical methods to discover
similarities between items. The high entry barriers of
providing good recommendations can be characterised
by the data acquisition problem [4]: providing
recommendations for (a) new items or for (b) new
users is a challenge if no data about the item or user is
available. If the number of ratings is low compared to
the number of items then (c) sparsity of the data will
lead to ineffective recommendations.
We propose an alternative to building closed
recommender systems: by utilising open data sources
from the Linking Open Data (LOD) community project,
it is possible to build open recommender systems,
which can mitigate the challenges introduced by the
data acquisition problem.
2. Background: Linked Data
Linked Data refers to a set of best practices for
publishing and connecting structured data on the Web
[2], by making semantic information about things and
concepts available via RDF and HTTP. They have been
adopted by a steadily growing number of data providers
which form the LOD cloud, e.g. DBpedia provides data
from Wikipedia pages, and both the US and UK
governments have converted data sets to RDF.
Social Web sites provide data, which is modeled
after the principle of object-centered sociality: it
connects individuals not just directly into communities,
but also indirectly via objects of a social focus, such as
a music act. Sites, which use the Friend-of-a-Friend
(FOAF) vocabulary to publish such data, include
MySpace and LiveJournal.
Figure 1: Applying collaborative filtering to Linked Data
2. Methodology
Figure 1 shows the steps of processing Linked Data
for collaborative recommendations: (1) integrating the
data about user-item connections from different sources
to a common vocabulary. (2) Transforming the
representation of the data from an RDF graph to a useritem
matrix. (3) Applying a specific collaborative
filtering algorithm on the user-item matrix.
This approach allows us to “fill in the gaps” in local
data, by using data with user-item connections from
external sources, thus mitigating the data acquisition
problem.
4. Evaluation
We have augmented the data from the closed Smart
Radio streaming recommendation service (190 users,
330 musicians) with Linked Data from MySpace,
adding 11000 users and 25000 new connections.
We evaluated a binary cosine similarity for the CF
algorithm, by using Last.fm as a “gold standard” [3].
The result of adding external data was an improvement
of precision from 2% to 14%, and recall from 7% to
33%.
5. References
[1] G. Adomavicius, and A. Tuzhilin, “Toward the next
generation of recommender systems: A survey of the state-ofthe-art
and possible extensions”, IEEE Transactions on
Knowledge and Data Engineering, 2005.
[2] C. Bizer, T. Heath and T. Berners-Lee, “Linked data-the
story so far”, Journal on Semantic Web and Information
Systems, 2009.
[3] J. Herlocker, J. Konstan et al., “Evaluating collaborative
filtering recommender systems”, ACM Transactions on
Information Systems, 2004.
[4] A. I. Schein, A. Popescul et al., “Methods and metrics for
cold-start recommendations”, Conference on Research and
Development in Information Retrieval, 2002.
* This extended abstract is based on B. Heitmann and C. Hayes, “Using Linked Data to Build Open, Collaborative Recommender
Systems”, AAAI Spring Symposia, 2010, and funded by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Líon-2).
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