Abstract
This paper presents a novel approach for Linked Data-based
recommender systems by means of semantic patterns. We associate to
each pattern the rating of the arrival book (0 or 1) and compute user
profiles by aggregating, for each book in the user training set, the ratings
of all the patterns pointing to that book. Ratings are aggregated by
estimating the expected value of a Beta distribution describing the rating
given to the book. Our approach allows the determination of a rating for
a book, even if the book is poorly connected with user profile. It allows
for a “prudent” estimation thanks to smoothing, obtained by using the
Beta distribution. If many patterns are available, it considers all the
contributions. Nevertheless, it allows for a lightweight computation of
ratings as it exploits the knowledge encoded in the patterns. Without
any setup of the system, this approach allowed us to reach a precision of
0.60 and an overall F-measure of about 0.52.
recommender systems by means of semantic patterns. We associate to
each pattern the rating of the arrival book (0 or 1) and compute user
profiles by aggregating, for each book in the user training set, the ratings
of all the patterns pointing to that book. Ratings are aggregated by
estimating the expected value of a Beta distribution describing the rating
given to the book. Our approach allows the determination of a rating for
a book, even if the book is poorly connected with user profile. It allows
for a “prudent” estimation thanks to smoothing, obtained by using the
Beta distribution. If many patterns are available, it considers all the
contributions. Nevertheless, it allows for a lightweight computation of
ratings as it exploits the knowledge encoded in the patterns. Without
any setup of the system, this approach allowed us to reach a precision of
0.60 and an overall F-measure of about 0.52.
Original language | English |
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Title of host publication | SemWebEval 2014 |
Editors | V. Presutti, M. Stankovic, E. Cambria, I. Cantador, A. Di Iorio, T. Di Noia, C. Lange, D. Reforgiato Recupero, A. Tordai |
Place of Publication | Heidelberg |
Publisher | Springer |
Pages | 182-187 |
Number of pages | 5 |
Publication status | Published - 2014 |
Publication series
Name | CCIS |
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Number | 475 |