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Personalization of Recommendation Diversification


W. G. Dulakshi Vihanga, K. L. Jayaratne, Andrea Barraza

Publication Type: 
Refereed Conference Meeting Proceeding
Accuracy of the recommendations has long been regarded as the primary quality aspect of Recommender Systems (RS), but there’s an increasing cognizance that there are other factors such as diversity that users also value. Despite the increased interest of researchers to improve diversification of recommendations, we find that personalization of diversification has been overlooked. As the preference for diversity changes from person-to-person, we propose a personalized diversification technique which is capable of controlling the trade-off between accuracy and diversity, where personalization is achieved by diversifying the recommendation list with more novel items if the user has shown diverse preferences in the past, and diversifying the recommendation list with more relevant items if the user has shown homogeneous preferences in the past. The proposed approach includes a re-ranking mechanism that generates the final diversified recommendation list by re-ranking the Top-N items generated from some traditional RS in a personalized manner that preserves diversity. Our experiments and evaluation provides evidence to illustrate the properties of proposed technique and indicate the proposed approach has comparable results to state-of-art techniques. Moreover, unlike other diversification techniques, our approach can make the diversification process personalized.
Conference Name: 
9th Annual International Conference on Computer Games, Multimedia & Allied Technology (CGAT)
Digital Object Identifer (DOI): 
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Conference Location: 
Research Group: 
National University of Ireland, Galway (NUIG)
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