Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108586
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Type: Conference paper
Title: A fast algorithm to build new users similarity list in neighbourhood-based collaborative filtering
Author: Lu, Z.
Shen, H.
Citation: Advances in Parallel and Distributed Computing and Ubiquitous Services, 2016 / vol.368, pp.229-236
Publisher: Springer
Issue Date: 2016
ISBN: 9789811000676
ISSN: 1876-1100
1876-1119
Conference Name: 4th International Conference on Ubiquitous Computing Application and Wireless Sensor Network (UCAWSN) (08 Jul 2015 - 10 Jul 2015 : Jeju, Korea)
Statement of
Responsibility: 
Recommender systems; Neighbourhood-based collaborative filtering; Similarity computation; Database applications
Abstract: Neighbourhood-based Collaborative Filtering (CF) has been applied in the industry for several decades because of its easy implementation and high recommendation accuracy. As the core of neighbourhood-based CF, the task of dynamically maintaining users’ similarity list is challenged by cold-start problem and scalability problem. Recently, several methods are presented on addressing the two problems. However, these methods require mn steps to compute the similarity list against the kNN attack, where m and n are the number of items and users in the system respectively. Observing that the k new users from the kNN attack, with enough recommendation data, have the same rating list, we present a faster algorithm, TwinSearch, to avoid computing and sorting the similarity list for each new user repeatedly to save the time. The computational cost of our algorithm is 1/125 of the existing methods. Both theoretical and experimental results show that the TwinSearch Algorithm achieves better running time than the traditional method.
Description: Lecture Notes in Electrical Engineering 368
Rights: © Springer Science+Business Media Singapore 2016
RMID: 0030044925
DOI: 10.1007/978-981-10-0068-3_30
Appears in Collections:Computer Science publications

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