Intent-aware Item-based Collaborative Filtering for Personalised Diversification

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Title: Intent-aware Item-based Collaborative Filtering for Personalised Diversification
Authors: Wasilewski, JacekHurley, Neil J.
Permanent link: http://hdl.handle.net/10197/9980
Date: 31-Aug-2017
Online since: 2019-04-16T10:39:21Z
Abstract: Diversity has been identified as one of the key dimensions of recommendation utility that should be considered besides the overall accuracy of the system. A common diversification approach is to rerank results produced by a baseline recommendation engine according to a diversification criterion. The intent-aware framework is one of the frameworks that has been proposed for recommendations diversification. It assumes existence of a set of aspects associated with items, which also represent user intentions, and the framework promotes diversity across the aspects to address user expectations more accurately. In this paper we consider item-based collaborative filtering and suggest that the traditional view of item similarity is lacking a user perspective. We argue that user preferences towards different aspects should be reflected in recommendations produced by the system. We incorporate the intent-aware framework into the item-based recommendation algorithm by injecting personalised intent-aware covariance into the item similarity measure, and explore the impact of such change on the performance of the algorithm. Our experiments show that the proposed method improves both accuracy and diversity of recommendations, offering better accuracy/ diversity tradeoff than existing solutions.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Keywords: Recommender systemsDiversificationUser expectationsIntent-aware recommendations
DOI: 10.1145/3209219.3209234
Other versions: http://ceur-ws.org/Vol-1905/
https://recsys.acm.org/recsys17/
Language: en
Status of Item: Peer reviewed
Is part of: Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (RecSys 2017)
ISBN: 978-1-4503-5589-6
Appears in Collections:Insight Research Collection

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