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

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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Active learning for multi-label classification based on sphere structured support vector machine

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.01359 ◽

2013 ◽

Vol 32 (5) ◽

pp. 1359-1361

Author(s):

Hua JIANG ◽

Yu-shun QI

Keyword(s):

Support Vector Machine ◽

Active Learning ◽

Support Vector ◽

Structured Support Vector Machine

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Fast Laplacian twin support vector machine with active learning for pattern classification

Applied Soft Computing ◽

10.1016/j.asoc.2018.10.042 ◽

2019 ◽

Vol 74 ◽

pp. 424-439 ◽

Cited By ~ 3

Author(s):

Reshma Rastogi ◽

Sweta Sharma

Keyword(s):

Support Vector Machine ◽

Active Learning ◽

Pattern Classification ◽

Twin Support Vector Machine ◽

Support Vector

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K-means based on active learning for support vector machine

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) ◽

10.1109/icis.2017.7960089 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jie Gan ◽

Ang Li ◽

Qian-Lin Lei ◽

Hao Ren ◽

Yun Yang

Keyword(s):

Support Vector Machine ◽

Active Learning ◽

Support Vector

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Support vector machine active learning by Hessian regularization

Journal of Visual Communication and Image Representation ◽

10.1016/j.jvcir.2017.08.001 ◽

2017 ◽

Vol 49 ◽

pp. 47-56 ◽

Cited By ~ 10

Author(s):

Weifeng Liu ◽

Lianbo Zhang ◽

Dapeng Tao ◽

Jun Cheng

Keyword(s):

Support Vector Machine ◽

Active Learning ◽

Support Vector

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Android malicious application detection using support vector machine and active learning

2017 13th International Conference on Network and Service Management (CNSM) ◽

10.23919/cnsm.2017.8256035 ◽

2017 ◽

Cited By ~ 3

Author(s):

Bahman Rashidi ◽

Carol Fung ◽

Elisa Bertino

Keyword(s):

Support Vector Machine ◽

Active Learning ◽

Support Vector

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Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios

International Journal of Technology and Educational Marketing ◽

10.4018/ijtem.2014010101 ◽

2014 ◽

Vol 4 (1) ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

M. Waseem Chughtai ◽

Imran Ghani ◽

Ali Selamat ◽

Seung Ryul Jeong

Keyword(s):

Collaborative Filtering ◽

Cold Start ◽

Traditional Education ◽

Experimental Result ◽

Personalized Recommendation ◽

Web Based ◽

Web Based Learning ◽

E Learning ◽

Learning Scenarios ◽

Content Based Filtering

Web-based learning or e-Learning in contrast to traditional education systems offer a lot of benefits. This article presents the Goal-based Framework for providing personalized similarities between multi users profile preferences in formal e-Learning scenarios. It consists of two main approaches: content-based filtering and collaborative filtering. Because only traditional content-based filtering is not sufficient to generate the recommendations for new-users, therefore, the proposed work hybridized multi user's collaborative filtering functionalities with personalized content-based profile preferences filtering. The main purpose of this proposed work is to (a) overcome the user-based cold-start profile recommendations and (b) improve the recommendations accuracy for new-users in formal e-learning recommendation systems. The experimental has been done by using the famous ‘MovieLens' dataset with 15.86% density of the user-item matrix with respect to ratings, while the evaluation of experimental results have been performed with precision mean and recall mean to test the effectiveness of Goal-based personalized recommendation framework. The Experimental result Precision: 81.90% and Recall: 86.56% show that the proposed framework goals performed well for the improvement of user-based cold-start issue as well as for content-based profile recommendations, using multi users personalized collaborative similarities, in formal e-Learning scenarios effectively.

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Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420590338 ◽

2020 ◽

Vol 34 (10) ◽

pp. 2059033

Author(s):

Gang Huang ◽

Man Yuan ◽

Chun-Sheng Li ◽

Yong-he Wei

Keyword(s):

Collaborative Filtering ◽

Domain Knowledge ◽

Cold Start ◽

Petroleum Exploration ◽

Personalized Recommendation ◽

Knowledge Graph ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm ◽

Exploration And Development

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

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Medical Image Retrieval Based on Semi-Supervised Learning

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.108-111.201 ◽

2010 ◽

Vol 108-111 ◽

pp. 201-206 ◽

Cited By ~ 1

Author(s):

Hui Liu ◽

Cai Ming Zhang ◽

Hua Han

Keyword(s):

Support Vector Machine ◽

Active Learning ◽

Image Retrieval ◽

Supervised Learning ◽

Relevance Feedback ◽

Medical Image ◽

Content Based Image Retrieval ◽

Support Vector ◽

Learning Support ◽

Medical Image Retrieval

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

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Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Journal of Hydrology and Hydromechanics ◽

10.2478/v10098-012-0002-7 ◽

2012 ◽

Vol 60 (1) ◽

pp. 16-32 ◽

Cited By ~ 3

Author(s):

Hamid Shahraiyni ◽

Mohammad Ghafouri ◽

Saeed Shouraki ◽

Bahram Saghafian ◽

Mohsen Nasseri

Keyword(s):

Support Vector Machine ◽

Active Learning ◽

Support Vector ◽

Learning Method ◽

Runoff Simulation ◽

Karoon River ◽

Discharge Data ◽

Daily Streamflow ◽

Runoff Modeling ◽

Active Learning Method

Comparison Between Active Learning Method and Support Vector Machine for Runoff ModelingIn this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic Algorithm (GA), as a well known datadriven model for long term simulation of daily streamflow in Karoon River. The daily discharge data from 1991 to 1996 and from 1996 to 1999 were utilized for training and testing of the models, respectively. Values of the Nash-Sutcliffe, Bias, R2, MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m3s-1, 0.81, 12.9%, and 1.9%, respectively. Following the same order of parameters, these criteria for optimized SVM model were 0.8, -10.7 m3s-1, 0.81, 7.3%, and -3.6%, respectively. The results show appropriate and acceptable simulation by ALM and optimized SVM. Optimized SVM is a well-known method for runoff simulation and its capabilities have been demonstrated. Therefore, the similarity between ALM and optimized SVM results imply the ability of ALM for runoff modeling. In addition, ALM training is easier and more straightforward than the training of many other data driven models such as optimized SVM and it is able to identify and rank the effective input variables for the runoff modeling. According to the results of ALM simulation and its abilities and properties, it has merit to be introduced as a new modeling method for the runoff modeling.

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