A social hybrid recommendation system using <scp>LSTM</scp> and <scp>CNN</scp>

Daneshvar, Hirad; Ravanmehr, Reza

doi:10.1002/cpe.7015

Cited by 14 publications

(3 citation statements)

References 58 publications

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“…Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) architecture that is designed to address the vanishing gradient problem, which can occur when training traditional RNNs. LSTMs were introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997 and have become popular for various tasks involving sequential data, such as Natural Language Processing (NLP), speech recognition, and time series analysis [19][20].…”

Section: Long Short-term Memory (Lstm) Approachmentioning

confidence: 99%

Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis

Ali,

Muhammad,

Al-Shamayleh

et al. 2023

IEEE Access

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Movie recommendation algorithms play an important role in assisting consumers in identifying films that match their own likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, have showed substantial promise in collecting sequential patterns to improve movie recommendations among the different techniques used for this purpose. Long Short-Term Memory-Inter Intra Metapath Aggregation (LSTM-IIMA) in movie recommendation systems is proposed in this study, with a specific focus on incorporating intra and inter metapath analysis. The intra metapath analysis investigates interactions within a single metapath, whereas the inter metapath analysis investigates links between numerous metapaths. Intra and inter metapath analyses are used in the LSTM-based movie recommendation system LSTM-IIMA to capitalise on these rich linkages. Each metapath sequence records the dependencies of a user's interactions with films and other things. The LSTM architecture has been modified to handle these metapath sequences, processing them to record temporal dependencies and entity interactions. To optimize the parameters and minimize prediction errors, the model is trained using supervised learning techniques. To measure the quality and usefulness of the recommendations, the LSTM-IIMA evaluation incorporates metrics such as precision, recall, ablation analysis, time efficiency and Area Under the Curve (AUC). The performance of the system is compared to that of alternative recommendation techniques HAN and MAGNN. Overall, incorporating intra and inter metapath analysis into the LSTM-IIMA improves its ability to capture complex linkages and dependencies between movies, users, and other things.

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Section: Long Short-term Memory (Lstm) Approachmentioning

confidence: 99%

Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis

Ali,

Muhammad,

Al-Shamayleh

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For example, Su et al [43] proposed a link prediction model based on Dempster-Shafer theory to compute implicit relationships of users in social recommender systems. Daneshvar and Ravanmehr [44] developed an idea recommendation algorithm model based on implicit trust relationship inference while incorporating temporal features into the system. Liu and He [45] used trust propagation and aggregation strategies to identify indirect trust of users in OICs.…”

Section: User Segmentation In Oicsmentioning

confidence: 99%

A User Segmentation Method in Heterogeneous Open Innovation Communities Based on Multilayer Information Fusion and Attention Mechanisms

Daradkeh

2022

Journal of Open Innovation: Technology, Market, and Complexity

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The heterogeneity and diversity of users and external knowledge resources is a hallmark of open innovation communities (OICs). Although user segmentation in heterogeneous OICs is a prominent and recurring issue, it has received limited attention in open innovation research and practice. Most existing user segmentation methods ignore the heterogeneity and embedded relationships that link users to communities through various items, resulting in limited accuracy of user segmentation. In this study, we propose a user segmentation method in heterogeneous OICs based on multilayer information fusion and attention mechanisms. Our method stratifies the OIC and creates user node embeddings based on different relationship types. Node embeddings from different layers are then merged to form a global representation of user fusion embeddings based on a semantic attention mechanism. The embedding learning of nodes is optimized using a multi-objective optimized node representation based on the Deep Graph Infomax (DGI) algorithm. Finally, the k-means algorithm is used to form clusters of users and partition them into distinct segments based on shared features. Experiments conducted on datasets collected from four OICs of business intelligence and analytics software show that our method outperforms multiple baseline methods based on unsupervised and supervised graph embeddings. This study provides methodological guidance for user segmentation based on structured community data and semantic social relations and provides insights for its practice in heterogeneous OICs.

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“…Teachers with less teaching experience cannot quickly select the appropriate curriculum design courseware and teaching process from a large number of courses [4]. Moreover, the self-study ability of students in higher vocational colleges is weaker than that of students in ordinary colleges [5]. Therefore, when they are faced with a large number of admirers, they may also have negative emotions such as confusion and helplessness.…”

Section: Introductionmentioning

confidence: 99%

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

Zheng,

2024

SCPE

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With the development and maturity of the Internet education industry, more and more vocational colleges have opened English teaching courses based on massive open online courses courses. However, there are many English-related courses on the massive open online courses course platform, and the use of scientific recommendation models can improve the teaching quality of such courses. Therefore, this research attempts to design two improved attention mechanisms and user-based embedded expression using meta-path technology. At the same time, these two are combined with reinforcement learning technology to design an improved massive open online courses English course recommendation model. The test results show that the hit rate of the model designed in this study is 89.84%, 74.28%, 70.81% and 71.35% respectively when the rank number is 20 and the parameter is 10. At this time, the cumulative income of normalized discount is 48.24%, 34.58%, 25.96% and 28.69% respectively. However, when the number of calculated samples reaches the maximum value of 1158609, the calculation time of the improved reinforcement learning recommendation model is 1867 seconds, which is also higher than the comparison model. The experimental results show that the curriculum recommendation accuracy of the massive open online courses recommendation model designed in this study is higher and the recommendation results are more reasonable. The results of this research have a certain application potential in the field of the construction of online education in colleges and universities.

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A social hybrid recommendation system using LSTM and CNN

Cited by 14 publications

References 58 publications

Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis

Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis

A User Segmentation Method in Heterogeneous Open Innovation Communities Based on Multilayer Information Fusion and Attention Mechanisms

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

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