Title :
Optimizing Personalized Ranking in Recommender Systems with Metadata Awareness
Author :
Manzato, Marcelo G. ; Domingues, Marcos A. ; Rezende, Solange O.
Author_Institution :
Math. & Comput. Inst., Univ. of Sao Paulo, Sao Carlos, Brazil
Abstract :
In this paper, we propose an item recommendation algorithm based on latent factors which uses implicit feedback from users to optimize the ranking of items according to individual preferences. The novelty of the algorithm is the integration of content metadata to improve the quality of recommendations. Such descriptions are an important source to construct a personalized set of items which are meaningfully related to the user´s main interests. The method is evaluated on two different datasets, being compared against another approach reported in the literature. The results demonstrate the effectiveness of supporting personalized ranking with metadata awareness.
Keywords :
data integration; meta data; optimisation; recommender systems; relevance feedback; content metadata integration; individual preferences; item ranking; item recommendation algorithm; metadata awareness; personalized ranking optimization; recommender systems; user feedback; Bayes methods; Business process re-engineering; Motion pictures; Optimization; Predictive models; Recommender systems; Training;
Conference_Titel :
Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on
Conference_Location :
Warsaw
DOI :
10.1109/WI-IAT.2014.33
