Personalised Diversification Using Intent-Aware Portfolio
Refereed Original Article
e intent-aware diversication 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 diversication 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 diversication capabilities of the proposed method on the MovieLens dataset.
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Date Accepted for Publication:
Tuesday, 6 June, 2017
UMAP 2017 conference. Late-Breaking Results track. Location: Bratislava, Slovakia, 9-12 July 2017.
National University of Ireland, Dublin (UCD)
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