| 41689 |
Creator |
21e3abf33e3daaa89c07ea7d5da24bb0 |
| 41689 |
Creator |
ext-e9e6079f02e13cd35a501d7ba4914ced |
| 41689 |
Creator |
ext-f1849a61df71fff8ed53982dfe06018d |
| 41689 |
Date |
2013 |
| 41689 |
Is Part Of |
repository |
| 41689 |
abstract |
Finding similar users in social communities is often challenging, especially in the
presence of sparse data or when working with heterogeneous or specialized domains.
When computing semantic similarity among users it is desirable to have a measure which
allows to compare users w.r.t. any concept in the domain. We propose such a technique
which reduces the problems caused by data sparsity, especially in the cold start phase,
and enables granular and context-based adaptive suggestions. It allows referring to
a certain set of most similar users in relation to a particular concept when a user
needs suggestions about a certain topic (e.g. cultural events) and to a possibly completely
different set when the user is interested in another topic (e.g. sport events). Our
approach first uses a variation of the spreading activation technique to propagate
the users’ interests on their corresponding ontology-based user models, and then computes
the concept-biased cosine similarity (CBC similarity), a variation of the cosine similarity
designed for privileging a particular concept in an ontology. CBC similarity can be
used in many adaptation techniques to improve suggestions to users. We include an
empirical evaluation on a collaborative filtering algorithm, showing that the CBC
similarity works better than the cosine similarity when dealing with sparse data. |
| 41689 |
authorList |
authors |
| 41689 |
presentedAt |
ext-a258c023a9ffc294b956a67504b2d59e |
| 41689 |
status |
peerReviewed |
| 41689 |
uri |
http://data.open.ac.uk/oro/document/279229 |
| 41689 |
uri |
http://data.open.ac.uk/oro/document/279230 |
| 41689 |
uri |
http://data.open.ac.uk/oro/document/279231 |
| 41689 |
uri |
http://data.open.ac.uk/oro/document/279232 |
| 41689 |
uri |
http://data.open.ac.uk/oro/document/279233 |
| 41689 |
uri |
http://data.open.ac.uk/oro/document/279234 |
| 41689 |
uri |
http://data.open.ac.uk/oro/document/282458 |
| 41689 |
volume |
8249 |
| 41689 |
type |
AcademicArticle |
| 41689 |
type |
Article |
| 41689 |
label |
Osborne, Francesco ; Likavec, Silvia and Cena, Federica (2013). Granular semantic
user similarity in the presence of sparse data. In: AI*IA 2013: Advances in Artificial
Intelligence: XIIIth International Conference of the Italian Association for Artificial
Intelligence, Turin, Italy, December 4-6, 2013. Proceedings, Lecture Notes in Computer
Science, Springer International Publishing, pp. 385–396. |
| 41689 |
label |
Osborne, Francesco ; Likavec, Silvia and Cena, Federica (2013). Granular semantic
user similarity in the presence of sparse data. In: AI*IA 2013: Advances in Artificial
Intelligence: XIIIth International Conference of the Italian Association for Artificial
Intelligence, Turin, Italy, December 4-6, 2013. Proceedings, Lecture Notes in Computer
Science, Springer International Publishing, pp. 385–396. |
| 41689 |
Publisher |
ext-6c8b7c40a5167b142d7fb1354cd46407 |
| 41689 |
Title |
Granular semantic user similarity in the presence of sparse data |
| 41689 |
in dataset |
oro |