000			01917naa a2200277 a 4500
003			AR-LpUFIB
005			20250311171137.0
008			230201s2011 xx o 000 0 eng d
024	8		_aDIF-M6753 _b6890 _zDIF006161
040			_aAR-LpUFIB _bspa _cAR-LpUFIB
100	1		_aDíaz, Alicia Viviana
245	1	0	_aQuality web information retrieval : _btowards Improving semantic recommender systems with friendsourcing
300			_a1 archivo (52,8 kB)
500			_aFormato de archivo: PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
520			_aWeb content quality is crucial in any domains, but it is even more critical in the health and e-learning ones. Users need to retrieve information that is precise, believable, and relevant to their problem. With the exponential growth of web contents, Recommender System has become indispensable for discovering quality information that might interest or be needed by web users. Quality-based Recommender Systems take into account quality criteria like credibility, believability, readability. In this paper, we present an approach to conceive Social Semantic Recommender Systems. In this approach a friendsourcing strategy is applied to better adequate recommendations to the user needs. The friendsourcing strategy focuses on the use of social force to assess quality of web content. In this paper we introduce the main research issues of this approach and detail the road-map we are following in the QHIR Project.
534			_aCongresso Ibero-americano de Telemática (CITA 2011). (6º : 2011 may.16-18 : Gramado, Brasil)
650		4	_aREDES SOCIALES _94685
650		4	_aFILTRADO
650		4	_aONTOLOGÍAS
700	1		_aMotz, Regina
700	1		_aFernández, Alejandro
700	1		_aLima, José Valdeni de
700	1		_aLópez, Diego
856	4	0	_uhttp://goo.gl/uSk51J
942			_cCP
999			_c55943 _d55943