Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/108544
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Type: Conference paper
Title: Using category and keyword for personalized recommendation: a scalable collaborative filtering algorithm
Author: Ji, K.
Shen, H.
Citation: Proceedings - International Symposium on Parallel Architectures, Algorithms and Programming, PAAP, 2014, pp.197-202
Publisher: IEEE
Issue Date: 2014
Series/Report no.: International Symposium on Parallel Architectures Algorithms and Programming
ISBN: 9781479938445
ISSN: 2168-3034
2168-3042
Conference Name: 6th International Symposium on Parallel Architectures, Algorithms, and Programming (PAAP) (13 Jul 2014 - 15 Jul 2014 : Beijing, China)
Statement of
Responsibility: 
Ke Ji, Hong Shen
Abstract: Scalability is another major issue for recommender systems except data sparsity and prediction quality. However, it has still not been well solved while many social recommendation models have been propose to improve the latter two problems. In this paper, we propose a scalable collaborative filtering algorithm based matrix factorization that introduce two common context factors: category and keyword besides social information. In the proposed model, we make prediction together using two preference matrices:user-category and user-keyword instead of only using the user-item rating matrix. This has the advantage that for new items, our model can make use of the two factors to make prediction, although they do not exist in the rating matrix. Experimental results on real dataset show that our model has a good scalability for new items, while still performing better than other state-of-art models.
Keywords: Collaborative Filtering; Matrix Factorization; Personalized; Social Recommendation; Graphical Model
Rights: © 2014 IEEE
DOI: 10.1109/PAAP.2014.40
Appears in Collections:Aurora harvest 3
Computer Science publications

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