Context-Based Collaborative Filtering for Citation Recommendation

Liu, Haifeng; Kong, Xiangjie; Bai, Xiaomei; Wang, Wei; Bekele, Teshome Megersa; Xia, Feng

doi:10.1109/access.2015.2481320

Cited by 106 publications

(72 citation statements)

References 31 publications

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“…Recommendation This paper [14] written by IEEE Member Haifeng Liu, Xiamei Bai Wang and Senior IEEE Member Teshome Megersa Beklee, Feng Xia. In this paper, they propose a novel citation recommendation strategy that utilizes just effectively gotten citation relations as source information.…”

Section: Context-based Collaborative Filtering For Citationmentioning

confidence: 99%

Data Science Recommendation System using Semantic Technology

Shah,

2019

IJEAT

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Data Science is a field in which multidisciplinary blend of data inference, algorithm development, and technology are combined in order to solve analytically complex problems. Data science field tend to focus on a complex and large algorithmic technological problem having data at the core. Students at the starting of the learning phase don’t know all the technological and algorithmic aspects related to data science. Consulting through faculty or knowing from external source helps them to proceed towards the needed expertise that they want to gain. Data science recommendation system using semantic web data-science ontology and service-oriented architecture is proposed in our work to recommend students the appropriate resources for their queries. Recommending student significant information concerning books, on-line documentation, software tools, public code repositories, and experts, tutors to be contacted based on student query or previous exam scores is the main objective of the work. Ontology based service-oriented web platform with a conversational user interface that use NLP (Natural Language Processing) to recommend possible resources is proposed and tested to be efficient

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Section: Context-based Collaborative Filtering For Citationmentioning

confidence: 99%

Data Science Recommendation System using Semantic Technology

Shah,

2019

IJEAT

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“…The neighborbased CF algorithm can be divided into two subcategories: user-based CF and item-based CF [25,26]. The execution process of the neighbor-based CF recommendation algorithm can be divided into the following three steps [27,28]: (a) calculate the similarity between an active user (or item) and other users (or items) through ratings on items from users; (b) select nearest neighbors for the active user (or item) according to the obtained similarity; (c) predict the ratings on the candidate items from the active user according to historical preference information of nearest neighbors, so as to produce recommended results.…”

Section: Neighbor-based Collaborative Filteringmentioning

confidence: 99%

“…Inspired by [4,14,26,27,53], we incorporate salton factors and trust relationships into cosine similarity calculation between users. The improved similarity calculation method is as follows (see (21)):…”

Section: Definition 2 Average Similarity Standard Deviation (Assd)mentioning

confidence: 99%

A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

Chen

Hua

Gao

et al. 2018

Mathematical Problems in Engineering

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Recommender systems are recently becoming more significant in the age of rapid development of the information technology and pervasive computing to provide e-commerce users’ appropriate items. In recent years, various model-based and neighbor-based approaches have been proposed, which improve the accuracy of recommendation to some extent. However, these approaches are less accurate than expected when users’ ratings on items are very sparse in comparison with the huge number of users and items in the user-item rating matrix. Data sparsity and high dimensionality in recommender systems have negatively affected the performance of recommendation. To solve these problems, we propose a hybrid recommendation approach and framework using Gaussian mixture model and matrix factorization technology. Specifically, the improved cosine similarity formula is first used to get users’ neighbors, and initial ratings on unrated items are predicted. Second, users’ ratings on items are converted into users’ preferences on items’ attributes to reduce the problem of data sparsity. Again, the obtained user-item-attribute preference data is trained through the Gaussian mixture model to classify users with the same interests into the same group. Finally, an enhanced social matrix factorization method fusing user’s and item’s social relationships is proposed to predict the other unseen ratings. Extensive experiments on two real-world datasets are conducted and the results are compared with the existing major recommendation models. Experimental results demonstrate that the proposed method achieves the better performance compared to other techniques in accuracy.

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“…The text of different research resources can be analysed for similarity or by finding relevant research resources based on their linking to other research resources. An author must cite appropriate and relevant previous studies to assist readers to have in-depth knowledge of a particular research resource [14]. Also, context-aware recommendation technique [15] further enhances recommendation of research resources by modeling and predicting researcher's preferences through the introduction of contextual information (variable) into the recommendation process as an explicit additional class of data [16,17] information improves the prediction accuracy of recommendation systems and also increases the performance of recommendation systems [18,19].…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Contextual Variables for Research Resources Recommender System

Isinkaye¹,

Folajimi²

2018

IJISA

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Abstract-Context-aware recommender system (CARS) is a promising technique for recommending research resources to users (researchers) by predicting their preferences (resources) under different situations. If the contextual information given to such a system is inappropriate, it will certainly have a negative effect on the nature of recommendation output generated by the system as well as making the system to have high dimensionality complexity. Currently, several CARS recommendation algorithms have been developed but they have failed to bring to bear the means and importance of experimentally validating the contextual information used in different domains of application of CARS. Hence, this paper experimentally validates the contextual variables in the domain of research resources by splitting a research resource (article) into three major sections (introduction, review and methodology). These sections are the contextual variables validated in order to authenticate their viability as context that could be used in recommending research resources based on the specific section of an article a researcher is interested in. The result of our experiment shows that irrespective of the domain of articles, journal articles have higher variability in their citations at introduction, very significant variability between the articles in the review and high variability in the methodology contextual variable respectively than the articles in the proceeding under the three contextual variables. This experiment shows that these three variables could be used as context .It also shows the percentage of splitting that could be used within journals and proceedings for context-aware research resources recommendations.

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Context-Based Collaborative Filtering for Citation Recommendation

Cited by 106 publications

References 31 publications

Data Science Recommendation System using Semantic Technology

Data Science Recommendation System using Semantic Technology

A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

Experimental Validation of Contextual Variables for Research Resources Recommender System

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