Browsing by Author "Wu, Xuan-Wei"

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Fast recommendation on latent collaborative relations
(2016-10-01)
Liu, Chien-Liang
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Wu, Xuan-Wei
;
資訊工程學系
;
工業工程與管理學系
;
Department of Computer Science
One important property of collaborative filtering recommender systems is that popular items are recommended disproportionately often because they provide extensive usage data and, thus, can be recommended to more users. Compared to popular products, the niches can be as economically attractive as mainstream fare for online retailers. The online retailers can stock virtually everything, and the number of available niche products exceeds the hits by several orders of magnitude. This work addresses accuracy, coverage and prediction time issues to propose a novel latent factor model called latent collaborative relations (LCR), which transforms the recommendation problem into a nearest neighbor search problem by using the proposed scoring function. We project users and items to the latent space, and calculate their similarities based on Euclidean metric. Additionally, the proposed model provides an elegant way to incorporate with locality sensitive hashing (LSH) to provide a fast recommendation while retaining recommendation accuracy and coverage. The experimental results indicate that the speedup is significant, especially when one is confronted with large-scale data sets. As for recommendation accuracy and coverage, the proposed method is competitive on three data sets. (C) 2016 Elsevier B.V. All rights reserved.
Large-scale recommender system with compact latent factor model
(2016-12-01)
Liu, Chien-Liang
;
Wu, Xuan-Wei
;
資訊工程學系
;
工業工程與管理學系
;
Department of Computer Science
This work devises a factorization model called compact latent factor model, in which we propose a compact representation to consider query, user and item in the model. The blend of information retrieval and collaborative filtering is a typical setting in many applications. The proposed model can incorporate various features into the model, and this work demonstrates that the proposed model can incorporate context-aware and content-based features to handle context-aware recommendation and cold-start problems, respectively. Besides recommendation accuracy, a critical problem concerning the computational cost emerges in practical situations. To tackle this problem, this work uses a buffer update scheme to allow the proposed model to process data incrementally, and provide a means to use historical data instances. Meanwhile, we use stochastic gradient descent algorithm along with sampling technique to optimize ranking loss, giving a competitive performance while considering scalability and deployment issues. The experimental results indicate that the proposed algorithm outperforms other alternatives on four datasets. (C) 2016 Elsevier Ltd. All rights reserved.