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Incremental Kernel Mapping Algorithms for Scalable Recommender Systems

Incremental Kernel Mapping Algorithms for Scalable Recommender Systems
Incremental Kernel Mapping Algorithms for Scalable Recommender Systems
Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given item. Kernel Mapping Recommender (KMR)system algorithms have been proposed, which offer state-of-the-art performance. One potential drawback of the KMR algorithms is that the training is done in one step and hence they cannot accommodate the incremental update with the arrival of new data making them unsuitable for the dynamic environments. From this line of research, we propose a new heuristic, which can build the model incrementally without retraining the whole model from scratch when new data (item or user) are added to the recommender system dataset. Furthermore, we proposed a novel perceptron type algorithm, which is a fast incremental algorithm for building the model that maintains a good level of accuracy and scales well with the data. We show empirically over two datasets that the proposed algorithms give quite accurate results while providing significant computation savings.
Ghazanfar, Mustansar
9452876d-46e5-4c7d-9120-c486f448632d
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Ghazanfar, Mustansar
9452876d-46e5-4c7d-9120-c486f448632d
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

Ghazanfar, Mustansar, Szedmak, Sandor and Prugel-Bennett, Adam (2011) Incremental Kernel Mapping Algorithms for Scalable Recommender Systems. IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Special Session on Recommender Systems in e-Commerce (RSEC), United States. 07 - 09 Nov 2011. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given item. Kernel Mapping Recommender (KMR)system algorithms have been proposed, which offer state-of-the-art performance. One potential drawback of the KMR algorithms is that the training is done in one step and hence they cannot accommodate the incremental update with the arrival of new data making them unsuitable for the dynamic environments. From this line of research, we propose a new heuristic, which can build the model incrementally without retraining the whole model from scratch when new data (item or user) are added to the recommender system dataset. Furthermore, we proposed a novel perceptron type algorithm, which is a fast incremental algorithm for building the model that maintains a good level of accuracy and scales well with the data. We show empirically over two datasets that the proposed algorithms give quite accurate results while providing significant computation savings.

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More information

Accepted/In Press date: 7 November 2011
Additional Information: Event Dates: 7-9 Nov 2011
Venue - Dates: IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Special Session on Recommender Systems in e-Commerce (RSEC), United States, 2011-11-07 - 2011-11-09
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 272802
URI: http://eprints.soton.ac.uk/id/eprint/272802
PURE UUID: a7c66a84-0de2-4e20-829c-a1191dcb3f00

Catalogue record

Date deposited: 18 Sep 2011 19:04
Last modified: 30 Sep 2020 16:31

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