Leveraging personality information to improve community recommendation in e‐learning platforms

Sun, Jianshan; Geng, Jie; Cheng, Xusen; Zhu, Mingyue; Xu, Qiang; Liu, Yunli

doi:10.1111/bjet.13011

Cited by 15 publications

(8 citation statements)

References 38 publications

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“…Recommendation methods have been widely used to ease information overload in different contexts, and are generally classified into three categories, namely, collaborative filtering (CF), CB and hybrid models [15,16]. CF methods recommend items to target users based on the preferences of their like-minded neighbours.…”

Section: Literature Review 21 General Recommendation Methodsmentioning

confidence: 99%

Incorporating heterogeneous information in deep learning with informative meta-paths for community recommendations

Deng

Han

2021

Journal of Information Science

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Communities of interest promote knowledge sharing and discovery in social network platforms. However, platform users face difficulties of finding suitable communities, given their increasing number. Although recommendations have been proposed to help users find communities of interest, these methods ignore or exclude heterogeneous interactions between users and communities. In addition, widely used meta-paths help capture the complex semantic relation among entities but heavily rely on domain knowledge. In this study, we propose a novel recommendation model based on informative meta-path discovery in heterogeneous information networks and deep learning. Users, communities, relevant items and their relations are considered as entities in a heterogeneous information network, from where informative meta-paths are extracted on the basis of information theory to measure user-community similarities. Finally, similarities are incorporated in a deep learning model to predict whether target users join candidate communities. The proposed recommendation model is evaluated and compared against baseline methods using two data sets. Results demonstrate the superior performance of the present model in terms of precision, recall and F score.

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Section: Literature Review 21 General Recommendation Methodsmentioning

confidence: 99%

Incorporating heterogeneous information in deep learning with informative meta-paths for community recommendations

Deng

Han

2021

Journal of Information Science

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“…The user's preference rating data is described in a matrix form, and the matrix is used as the input item of the keyword projection model. The selection relationship between users and resources can reflect the interest trend among users [26][27][28], and the relationship between resource characteristics and user preference characteristics in college online is displayed by a dual-mode network. As far as a specific user group is concerned, when a user is associated with multiple network activities, multidimensional connections will be generated between users in the group, forming a multimodal network of selection relationships between users and resources.…”

Section: Keyword Projection Model For Different User Preferencementioning

confidence: 99%

Recommendation Method of Online Teaching Resources in Colleges and Universities Considering Different User Preference Factors

Zhang

2022

Wireless Communications and Mobile Computing

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In order to improve the recommendation performance of online teaching resources in colleges and universities and the learning efficiency of users, this paper considers user preference factors and studies the recommendation method of online teaching resources in colleges and universities. This paper selects different user preference factors and extracts preference keywords and builds a keyword projection model for different user preference resources based on this. In this paper, Markov is used to construct a keyword probability model, and the TF-IDF algorithm is introduced to refine online teaching resources. According to the calculated closeness of the resources required by the user, this paper realizes the accurate recommendation of resources. The experimental results show that each recommendation performance index of this method has reached a high value. The recommendation accuracy rate of this algorithm is the highest at 82.2%, the recall rate is the highest at 78.7%, the F value is the highest at 83.5%, and the average absolute error is lower than 0.56. And the successful recommendation rate of this method is as high as 97.6%.

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“…The sub dimension of "domain knowledge" is deleted because the domain knowledge level of an academic user has been reflected in the attributes of age, identity, education and major. Deleting this dimension can reduce the workload and avoid defining evaluation indicators, so as to avoid the error loss caused by subjective judgment [25]. According to the multi-dimensional academic user portrait model, the reconstructed and fused user attribute vector and its values are shown in Table IV.…”

Section: A User Attribute Vectormentioning

confidence: 99%

Research on Personalized Recommendation of High-Quality Academic Resources based on user Portrait

Xu¹,

Man²,

Sabri³

et al. 2022

IJACSA

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With the advent of the era of big data, the phenomenon of information overload is becoming increasingly serious. It is difficult for academic users to obtain the information they want quickly and accurately in the face of massive academic resources. Aiming at the optimization of academic resource recommendation services, this paper constructs a multidimensional academic user portrait model and proposes an Academic Resource Recommendation Algorithm Based on user portrait. This paper first, combs the relevant literature and information; Secondly, to obtain the attribute tags of multidimensional user portraits, a set of questionnaires are designed to collect the real information of academic users, and the corresponding academic user portrait model is constructed; Then, the collected data is processed through certain rules, and the user is quantitatively modeled based on the data through mathematical means; Finally, through the construction of the completed academic user portrait model, combined with collaborative filtering algorithm, provide personalized academic resource recommendation services for academic users. Through the verification and analysis of simulation experiments, the Academic Resource Recommendation Algorithm Based on the user portrait proposed in this paper plays a great role in expanding users' interest fields and discovering new hobbies across fields and disciplines.

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Leveraging personality information to improve community recommendation in e‐learning platforms

Cited by 15 publications

References 38 publications

Incorporating heterogeneous information in deep learning with informative meta-paths for community recommendations

Incorporating heterogeneous information in deep learning with informative meta-paths for community recommendations

Recommendation Method of Online Teaching Resources in Colleges and Universities Considering Different User Preference Factors

Research on Personalized Recommendation of High-Quality Academic Resources based on user Portrait

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