Other Open Access
SemRevRec: A Recommender System based on User Reviews and Linked Data
Vagliano, Iacopo;
Monti, Diego;
Morisio, Maurizio
Traditionally, recommender systems exploit user ratings to infer preferences. However, the growing popularity of social platforms has encouraged users to write textual reviews about liked items. These reviews represent a valuable source of non-trivial information that could improve users' decision processes. In this paper we propose a novel recommendation approach based on the semantic annotation of entities mentioned in user reviews and on the knowledge available in the Web of Data. We compared our recommender system with two baseline algorithms and a state-of-the-art Linked Data based approach. Our system provided more diverse recommendations with respect to the other techniques considered, while obtaining a better accuracy than the Linked Data based method.
| Name | Size | |
|---|---|---|
|
main_acm.pdf
md5:0f2d078fe7626326a39fa221c828d7f4 |
496.4 kB | Download |
| All versions | This version | |
|---|---|---|
| Views | 118 | 118 |
| Downloads | 53 | 53 |
| Data volume | 26.3 MB | 26.3 MB |
| Unique views | 107 | 107 |
| Unique downloads | 51 | 51 |
- Publication date:
- January 23, 2018
- DOI:
-
- Grants:
-
European Commission:
- MOVING - Training towards a society of data-savvy information professionals to enable open leadership innovation (693092)
- Meeting:
- Poster Proceeding of ACM Recsys 2017 (Poster-Recsys 2017), Como, Italy, 28 August 2017
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International