|
Title:
|
A NOVEL RECOMMENDER SYSTEM INSPIRED BY CONTEXTUAL ADVERTISING |
|
Author(s):
|
Andrea Addis, Giuliano Armano, Alessandro Giuliani, Eloisa Vargiu |
|
ISBN:
|
978-972-8939-23-6 |
|
Editors:
|
António Palma dos Reis and Ajith P. Abraham |
|
Year:
|
2010 |
|
Edition:
|
Single |
|
Keywords:
|
Internet services; applications; Information Retrieval; Recommender Systems; Contextual Advertising. |
|
Type:
|
Full Paper |
|
First Page:
|
67 |
|
Last Page:
|
74 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
So far, contextual advertising and recommender systems have been separately studied. Nevertheless, from a general perspective, nothing prevents from viewing contextual advertising as a kind of Web recommendation, aimed at embedding into a Web page the most relevant textual ads available for it. In fact, they share common aspects, the task of suggesting an advertising being a particular case of recommending an item (the advertising) to a user (the web page), and vice versa. In particular, we envision that bringing ideas from contextual advertising could help in building novel recommender systems with improved performance. In this paper, we propose a novel recommender system inspired by a generic solution typically adopted to solve contextual advertising tasks. To assess the effectiveness of the approach, we devised a hybrid system that embeds the proposed recommender system and a state-of-the-art item-based system. Results highlight that the proposed approach is effective in improving the recommendations issued by the item-based system. |
|
|
|
|
|
|
