Authors:
Giuliano Armano
and
Eloisa Vargiu
Affiliation:
University of Cagliari, Italy
Keyword(s):
Contextual advertising, Recommender systems, Information Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
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, the task of suggesting an advertising is a particular case of recommending an item (the advertising) to a user (the web page), and vice versa. We envision that bringing ideas from contextual advertising could help in building novel recommender systems with improved performance, and vice versa. To this end, in this paper, we propose a unifying view of contextual advertising and recommender systems. To this end, we suggest: (i) a way to build a recommender system inspired by a generic solution typically adopted to solve contextual advertising tasks and (ii) a way to realize a collaborative contextual advertising system a la mode of collaborative filtering.