Improved Collaborative Filtering Algorithm Using Topic Model

Na, Liu; Lü, Ying; Tang, Xiaojun; Wang, Haiwen; Xiao, Peng; Ming-Xia, Li

doi:10.1109/pdcat.2016.079

Cited by 5 publications

(3 citation statements)

References 9 publications

(2 reference statements)

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“…(1) Employ machine learning algorithm to predict unrated data, such as naive Bayesian [7], support vector machine (SVM) [8], neural networks [9], topic model [10], and deep learning collaborative filtering algorithms [11].…”

Section: Related Workmentioning

confidence: 99%

A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

Wang

Dai

et al. 2020

Discrete Dynamics in Nature and Society

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In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.

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Section: Related Workmentioning

confidence: 99%

A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

Wang

Dai

et al. 2020

Discrete Dynamics in Nature and Society

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“…Liu Na, et al [9] presented the improved collaborative filtering algorithm using topic model. They wanted to recommend item to target users.…”

Section: Related Workmentioning

confidence: 99%

“…We compared the result from our proposed method with 2 baseline methods including recommendation by CBF technique integrating with LDA [12] and recommendation by CF technique integrating with LDA [9] on the same data set. Both compared methods are already explained in related work section in Chapter 2.…”

Section: Recommending Hotel To Target Usersmentioning

confidence: 99%

A Hotel Hybrid Recommendation Method Based On Context-Driven Using Latent Dirichlet Allocation

Nimchaiyanan

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Recommender systems play an important role in helping users find items that they want. Normally, ratings are used in content-based filtering (CBF) and collaborative filtering (CF) for recommendation. However, only ratings are not enough for recommendation. Thus, contextual information, context driven and Latent Dirichlet Allocation (LDA) are used to improve recommendation. Also, the context of individual user has changed in the timeline (context-driven). In this work, a hotel hybrid recommendation method (CF+CBF) based on context-driven using LDA is proposed. Firstly, we find missing user ratings of user-hotel rating matrix by applying LDA on user ratings in order to get predicted score of hotels for the target user. Secondly, we find a group of users similar to the target user (neighbors). Then, we apply context-driven to recommend hotels that meet current interest of the target user. To evaluate the proposed method, we compare our proposed methods either CBF or CF integrating with LDA by measuring nDCG. The result shows that our proposed method outperforms all comparable methods in result accuracy.

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Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network

Pornwattanavichai

sakolnagara

Jirachanchaisiri

et al. 2019

Unsupervised and Semi-Supervised Learning

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Improved Collaborative Filtering Algorithm Using Topic Model

Cited by 5 publications

References 9 publications

A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

A Hotel Hybrid Recommendation Method Based On Context-Driven Using Latent Dirichlet Allocation

Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network

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