Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems

Rodpysh, Keyvan Vahidy; Mirabedini, Seyed Javad; Banirostam, Touraj

doi:10.1007/s10660-021-09488-7

Cited by 10 publications

(4 citation statements)

References 57 publications

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“…TCFACO is a method presented to reduce sparsity and cold user concerns using an explicit trust (Parvin, Moradi, & Esmaeili, 2019a). The authors introduced a context-aware recommendation algorithm called CSSVD to increase recommendation performance and mitigate the cold start and sparse data challenges (Rodpysh, Mirabedini, & Banirostam, 2021). They propose T-MRGF, a trust-ware recommendation method based on the fusion of heterogeneous multi-relational graphs.…”

Section: Trust-based Recommender Systemmentioning

confidence: 99%

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Khaledian

Nazari

Khamforoosh

et al. 2023

Expert Systems with Applications

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Section: Trust-based Recommender Systemmentioning

confidence: 99%

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Khaledian

Nazari

Khamforoosh

et al. 2023

Expert Systems with Applications

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“…By adding multimodal embedding into latent components to develop better feature representations, our proposed model outperformed the baseline techniques. Direct comparison experiments were conducted to judge the performance of the suggested DTLME model when compared against CSSVD [ 58 ], TF [ 59 ], and BPR [ 60 ] algorithms. Precision, Recall, F1-score, and MAE measures were used to evaluate the performance of the specified methods.…”

Section: Results and Analysismentioning

confidence: 99%

Deep transfer learning with multimodal embedding to tackle cold-start and sparsity issues in recommendation system

et al. 2022

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Recommender systems (RSs) have become increasingly vital in the modern information era and connected economy. They play a key role in business operations by generating personalized suggestions and minimizing information overload. However, the performance of traditional RSs is limited by data sparseness and cold-start issues. Though deep learning-based recommender systems (DLRSs) are very popular, they underperform when considering rating matrices with sparse entries. Despite their performance improvements, DLRSs also suffer from data sparsity, cold start, serendipity, and generalizability issues. We propose a multistage model that uses multimodal data embedding and deep transfer learning for effective and personalized product recommendations, and is designed to overcome data sparsity and cold-start issues. The proposed model includes two phases. In the first—offline—phase, a deep learning technique is implemented to learn hidden features from a large image dataset (targeting new item cold start), and a multimodal data embedding is used to produce dense user feature and item feature vectors (targeting user cold start). This phase produces three different similarity matrices that are used as inputs for the second—online—phase to generate a list of top-n relevant items for a target user. We analyzed the accuracy and effectiveness of the proposed model against the existing baseline RSs using a Brazilian E-commerce dataset. The results show that our model scored 0.5882 for MAE and 0.4011 for RMSE which is lower than baseline RSs which indicates that the model achieved an improved accuracy and was able to minimize the typical cold start and data sparseness issues during the recommendation process.

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“…Prediction accuracy and recommendation coverage are essential in testing whether a recommendation system can meet users' expectations [17] . Dynamic recommendations are vital in real-time recommendations for modern recommendation systems [18] . As can be seen from the table, the item-based multi-criteria CF and knowledge state model recommendations (KSMR) have the most comprehensive prediction accuracy and recommendation coverage at high sparsity but take slightly longer.…”

Section: Comparison and Analysis Of Recommendation Algorithmsmentioning

confidence: 99%

Intelligent learning application based on optical character recognition and personalized recommendation algorithms

Zhang,

pan,

Wang

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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This paper proposes an intelligent learning application based on OCR (optical character recognition) text recognition and a personalized recommendation algorithm, which can help students to record their mistakes and recommend similar questions according to the type of mistakes so that students can further consolidate their mistakes and improve their correctness rate in their daily work. This paper is based on the JAVA language for development and design. The user side is presented in the form of a wechat applet, which reduces the use threshold and increases the user's dependability. The project combines the functional features of OCR algorithms for regular and irregular text recognition. It adds the ability to recognize distorted and blurred fonts to print fonts, scanned text, and other horizontally positioned text, making it more relevant to everyday use scenarios. The personalized recommendation algorithm of the project adopts various recommendation algorithms respectively, among which the collaborative recommendation algorithm is divided into userbased collaborative Filtering and item-based multi-criteria collaborative filtering, which perform relevant recommendations for user positive feedback exercises and the collaborative recommendation algorithm divided into user-based collaborative filtering and item-based multi-criteria collaborative filtering, which can predict users' preferences more accurately. The project also incorporates an exercise recommendation method that models the user's knowledge state, which models the user's personalized knowledge state based on their daily problem-solving habits, and makes exercise recommendations based on the model, which is a good predictor.

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Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems

Cited by 10 publications

References 57 publications

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Deep transfer learning with multimodal embedding to tackle cold-start and sparsity issues in recommendation system

Intelligent learning application based on optical character recognition and personalized recommendation algorithms

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