Abstract
This paper presents an approach of recommending a ranked list of books to a user. A user profile is defined by a few liked and disliked books. To recommend a book, we calculate semantic relatedness of the given book to the liked and disliked books by using Wikipedia. Based on the obtained scores, we predict ratings of the book. We evaluate our approach on a dataset that consists of 6,181 users, 8,171 books and 67,990 user-item pairs to predict the rating.
| Original language | English |
|---|---|
| Journal | CEUR Workshop Proceedings |
| Volume | 1486 |
| Publication status | Published - 2015 |
| Event | ISWC 2015 Posters and Demonstrations Track, ISWC-P and D 2015 - co-located with the 14th International Semantic Web Conference, ISWC 2015 - Bethlehem, United States Duration: 11 Oct 2015 → … |
Keywords
- Semantic Relatedness
- Top N Recommendations
- Wikipedia