FRFP: A Friend Recommendation Method Based on Fine-Grained Preference

Shao, Mingmin; Jiang, Wenjun; Zhang, Lei

doi:10.1007/978-981-15-1301-5_4

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Miao et al [8] showed that online interactions will help to increase users' sense of social presence and further influence learning engagement. Shao et al [9] proposed a friend recommendation method based on fine-grained interest feature labels, which can leverage the labelling system of a learning community for learning partner recommendation. Hu et al [10] considered the dynamic interaction between students and learning content to propose a learning partner recommendation framework based on a convolutional neural network and dynamic interaction tripartite graphs.…”

Section: Introductionmentioning

confidence: 99%

Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

Shou,

Chen,

Wen

et al. 2024

Electronics

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The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners’ online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students’ current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner’s human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods.

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Section: Introductionmentioning

confidence: 99%

Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

Shou,

Chen,

Wen

et al. 2024

Electronics

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Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

Shao

Jiang

et al. 2022

J. Comput. Sci. Technol.

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Friend recommendation plays a key role in promoting user experience in online social networks (OSNs). However, existing studies usually neglect users' fine-grained interest as well as the evolving feature of interest, which may cause unsuitable recommendation. In particular, some OSNs, such as the online learning community, even have little work on friend recommendation. To this end, we strive to improve friend recommendation with fine-grained evolving interest in this paper. We take the online learning community as an application scenario, which is a special type of OSNs for people to learn courses online. Learning partners can help improve learners' learning effect and improve the attractiveness of platforms. We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest (LPRF-E for short). We extract a sequence of learning interest tags that changes over time. Then, we explore the time feature to predict evolving learning interest. Next, we recommend learning partners by fine-grained interest similarity. We also refine the learning partner recommendation framework with users' social influence (denoted as LPRF-F for differentiation). Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy (i.e., approximate 50% improvements on the precision and the recall) and can recommend learning partners with high quality (e.g., more experienced and helpful). Supplementary Information The online version contains supplementary material available at 10.1007/s11390-021-2124-z.

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Recommendation System Comparative Analysis: Internet of Things aided Networks

Shafik

Matinkhah

Shokoor

2022

EAI Endorsed Trans IoT

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Today, the public is not willing to spend much time identifying their personal needs. Therefore, it needs a system that automatically recommends customized items to customers. The Recommender system has an internet of things (IoT) that entails a subclass of evidenced-based sieving structures that pursues to forecast the assessment of a customer would stretch to an item. Within social networks, numerous categories of RS operate on different recommendation expertise. In this state-of-the-art, we describe and classify current studies from three different aspects by describing different methods of recommender systems. The Friend Recommendation System in social networks is necessary and inevitable, and it is due to this kind of coordination that inevitably recommends latent friends to customers. Making recommendations for friends is an imperative assignment for community networks, as obligating supplementary networks customarily superiors to enhanced customer experience.

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FRFP: A Friend Recommendation Method Based on Fine-Grained Preference

Cited by 3 publications

References 19 publications

Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

Recommendation System Comparative Analysis: Internet of Things aided Networks

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