Name
Abstract | ||
|---|---|---|
Due to the emergence and the prevalence of social networks, social interactions are found beneficial for Recommender Systems. Obviously, users can now rate items, comment and suggest them to friends through social networks. Therefore, these users behaviors must be integrated to predict her preferences. However, most of the works proposed in the literature integrate only users ratings in recommendation process and ignore other behaviors made by users while/after seeing an item. In this paper, we propose a new approach that integrates in a generic way all the user behaviors in order to predict her interests. We conduct a comprehensive effectiveness evaluation on real dataset crawled from Pinhole platform. We consider several social behaviors such as comment, time spent, recommendations and shares. We evaluate the impact of each behavior in the prediction accuracy. Experimental results demonstrate the importance of all social behaviors and the effectiveness of our approach compared to collaborative filtering rating-based and time-spent-based approaches. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/AICCSA.2017.153 | 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) |
Keywords | Field | DocType |
Recommender Systems,User behavior,Analytic Hierarchy Process,Collaborative Filtering | Recommender system,Social behavior,Collaborative filtering,Social network,Task analysis,Computer science,Computer network,Human–computer interaction,Prediction algorithms | Conference |
ISSN | ISBN | Citations |
2161-5322 | 978-1-5386-3582-7 | 0 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chayma Amri | 1 | 0 | 0.34 |
| Mariem Bambia | 2 | 0 | 0.34 |
| Rim Faiz | 3 | 98 | 36.23 |