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Author: search.filters.author.Liu, Chien-LiangSubject: search.filters.subject.Latent factor model

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    Background music recommendation based on latent factors and moods
    (2018-11-01)
    Liu, Chien-Liang
    ;
    Chen, Ying-Chuan
    ;
    資訊工程學系
    ;
    工業工程與管理學系
    ;
    Department of Computer Science
    Many mobile devices are equipped with video shooting function, and users tend to use these mobile devices to produce user generated content (UGC), and share with friends or the public owing to the popularity of social media. To make the video to be attractive, embedding appropriate background music into the video is a popular way to enrich user experience, but it is a time-consuming and labor-intensive task to find music that fits the video. This work proposes to use latent factors to recommend a list of music songs for a given video, in which the recommendation is based on the proposed score function, which involves the weighted average of the latent factors for the video and music. Moreover, we use pairwise ranking to design the objective function, and use stochastic gradient descent to optimize the proposed objective function. In the experiments, we specify two hypotheses and design several experiments to assess the performance and the effectiveness of the proposed algorithm from different aspects, including accuracy, quantitative research, and qualitative research. The experimental results indicate that the proposed model is promising in accuracy and quantitative research. Furthermore, this work provides detailed analysis to investigate the fitness of the background music that recommended by the system through interviewing the subjects.
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    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.
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    Fast recommendation on latent collaborative relations
    (2016-10-01)
    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
    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.