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Authors: Nima Joorabloo 1 ; Mahdi Jalili 1 and Yongli Ren 2

Affiliations: 1 School of Engineering, RMIT University, Swanston, Melbourne and Australia ; 2 School of Science, RMIT University, Swanston, Melbourne and Australia

Keyword(s): Recommendation System, Sequential Pattern, Similarity Measure, Time.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Collaborative Filtering ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems

Abstract: Recommender systems have significant applications in both industry and academia. Neighbourhood-based collaborative Filtering methods are the most widely used recommenders in industrial applications. These algorithms utilize preferences of similar users to provide suggestions for a target user. Users’ preferences often vary over time and many traditional collaborative filtering algorithms fail to consider this important issue. In this paper, a novel recommendation method is proposed based on predicting similarity between users in the future and forecasting their similarity trends over time. The proposed method uses the sequence of users’ ratings to predict the similarities between users in the future and use the predicted similarities instead of the original ones to detect users’ neighbours. Experimental results on benchmark datasets show that the proposed method significantly outperforms classical and state-of-the-art recommendation methods.

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Paper citation in several formats:
Joorabloo, N.; Jalili, M. and Ren, Y. (2019). A New Temporal Recommendation System based on Users’ Similarity Prediction. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR; ISBN 978-989-758-382-7; ISSN 2184-3228, SciTePress, pages 555-560. DOI: 10.5220/0008377205550560

@conference{kdir19,
author={Nima Joorabloo. and Mahdi Jalili. and Yongli Ren.},
title={A New Temporal Recommendation System based on Users’ Similarity Prediction},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR},
year={2019},
pages={555-560},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008377205550560},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR
TI - A New Temporal Recommendation System based on Users’ Similarity Prediction
SN - 978-989-758-382-7
IS - 2184-3228
AU - Joorabloo, N.
AU - Jalili, M.
AU - Ren, Y.
PY - 2019
SP - 555
EP - 560
DO - 10.5220/0008377205550560
PB - SciTePress

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