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Personalised Diversification Using Intent-Aware Portfolio

Publication Type: 
Refereed Original Article
Œe intent-aware diversi€cation framework considers a set of aspects associated with items to be recommended. A baseline recommendation is greedily re-ranked using an objective that promotes diversity across the aspects. In this paper the framework is analysed and a new intent-aware objective is derived that considers the minimum variance criterion, connecting the framework directly to portfolio diversi€cation from €nance. We derive an aspect model that supports the goal of minimum variance and that is faithful to the underlying baseline algorithm. We evaluate diversi€cation capabilities of the proposed method on the MovieLens dataset.
Digital Object Identifer (DOI): 
Publication Status: 
In Press
Date Accepted for Publication: 
Tuesday, 6 June, 2017
Publication Date: 
UMAP 2017 conference. Late-Breaking Results track. Location: Bratislava, Slovakia, 9-12 July 2017.
Research Group: 
National University of Ireland, Dublin (UCD)
Open access repository: