Skip to main content
Erschienen in:

21.09.2023

Collaborative filtering integrated fine-grained sentiment for hybrid recommender system

verfasst von: Rawaa Alatrash, Rojalina Priyadarshini, Hadi Ezaldeen

Erschienen in: The Journal of Supercomputing | Ausgabe 4/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning Recommender Systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. Users posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. Innovative has been made in this work to propose a new e-learning recommendation system based on individualization and FSA. A new framework is proposed based on collaborative filtering models (CFMs) integrating with fine-grained sentiment analysis (FSA) for hybrid recommendation (CFISAR) for effective recommendations. CFMs attempt to capture the learner's latent factors based on their selections of interest to build the learner profile. FSA models are introduced to deliver e-content with the highest ranked ratings related to the learner’s area and interests based on the extracted learner model. Moreover, a new approach is proposed to update the system continuously and not keep it bound to certain items by adding new books, where the initial rating of these new books is predicted based on FSA models. CFISAR is explored utilizing six CFMs to generate the prediction matrix and derive the learner model, resulting in a low MSE of 0.699 for Asymmetric SVD. The system used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context, and leveraging the goodness of deep learning, which predicted an accuracy of 0.9264% for the Peephole algorithm, that performed better than other models.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Bhanuse R, Mal S (2021) A systematic review: deep learning based e-learning recommendation system. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, pp 190–197 Bhanuse R, Mal S (2021) A systematic review: deep learning based e-learning recommendation system. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, pp 190–197
3.
Zurück zum Zitat Ezaldeen H, Misra R, Alatrash R, Priyadarshini R (2020) Semantically enhanced machine learning approach to recommend e-learning content. Int J Electron Bus 15(4):389–413CrossRef Ezaldeen H, Misra R, Alatrash R, Priyadarshini R (2020) Semantically enhanced machine learning approach to recommend e-learning content. Int J Electron Bus 15(4):389–413CrossRef
4.
Zurück zum Zitat Ezaldeen H, Misra R, Alatrash R, Priyadarshini R (2019) Machine learning based improved recommendation model for E-learning. In: 2019 International Conference on Intelligent Computing and Remote Sensing (ICICRS). IEEE, pp 1–6 Ezaldeen H, Misra R, Alatrash R, Priyadarshini R (2019) Machine learning based improved recommendation model for E-learning. In: 2019 International Conference on Intelligent Computing and Remote Sensing (ICICRS). IEEE, pp 1–6
6.
Zurück zum Zitat Mounika A, Saraswathi S (2021) Design of book recommendation system using sentiment analysis. Evolutionary computing and mobile sustainable networks. Springer, Singapore, pp 95–101CrossRef Mounika A, Saraswathi S (2021) Design of book recommendation system using sentiment analysis. Evolutionary computing and mobile sustainable networks. Springer, Singapore, pp 95–101CrossRef
7.
Zurück zum Zitat Alatrash R, Priyadarshini R, Ezaldeen H, Alhinnawi A (2022) Augmented language model with deep learning adaptation on sentiment analysis for E-learning recommendation. Cogn Syst Res 75:53–69CrossRef Alatrash R, Priyadarshini R, Ezaldeen H, Alhinnawi A (2022) Augmented language model with deep learning adaptation on sentiment analysis for E-learning recommendation. Cogn Syst Res 75:53–69CrossRef
12.
Zurück zum Zitat Bobadilla JESUS, Serradilla F, Hernando A (2009) Collaborative filtering adapted to recommender systems of e-learning. Knowl-Based Syst 22(4):261–265CrossRef Bobadilla JESUS, Serradilla F, Hernando A (2009) Collaborative filtering adapted to recommender systems of e-learning. Knowl-Based Syst 22(4):261–265CrossRef
13.
Zurück zum Zitat Zapata A, Menéndez VH, Prieto ME, Romero C (2015) Evaluation and selection of group recommendation strategies for collaborative searching of learning objects. Int J Hum Comput Stud 76:22–39CrossRef Zapata A, Menéndez VH, Prieto ME, Romero C (2015) Evaluation and selection of group recommendation strategies for collaborative searching of learning objects. Int J Hum Comput Stud 76:22–39CrossRef
14.
Zurück zum Zitat Klašnja-Milićević A, Ivanović M, Vesin B, Budimac Z (2018) Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Appl Intell 48(6):1519–1535CrossRef Klašnja-Milićević A, Ivanović M, Vesin B, Budimac Z (2018) Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Appl Intell 48(6):1519–1535CrossRef
15.
Zurück zum Zitat Mondal B, Patra O, Mishra S, Patra P (2020) A course recommendation system based on grades. In: 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE, pp 1–5 Mondal B, Patra O, Mishra S, Patra P (2020) A course recommendation system based on grades. In: 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE, pp 1–5
16.
Zurück zum Zitat Koffi DDASL, Ouattara N, Mambe DM, Oumtanaga S, Assohoun ADJE (2021) Courses recommendation algorithm based on performance prediction in E-learning. IJCSNS 21(2):148 Koffi DDASL, Ouattara N, Mambe DM, Oumtanaga S, Assohoun ADJE (2021) Courses recommendation algorithm based on performance prediction in E-learning. IJCSNS 21(2):148
17.
Zurück zum Zitat Jeevamol J, Renumol VG (2021) An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem. Educ Inf Technol 26:4993–5022CrossRef Jeevamol J, Renumol VG (2021) An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem. Educ Inf Technol 26:4993–5022CrossRef
21.
Zurück zum Zitat Turnip R, Nurjanah D, Kusumo DS (2017) Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners’ rating. In: 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e). IEEE, pp 61–66 Turnip R, Nurjanah D, Kusumo DS (2017) Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners’ rating. In: 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e). IEEE, pp 61–66
22.
Zurück zum Zitat Vaishali F, Archana G, Monika G, Vidya G, Sanap M (2016) E-learning recommendation system using fuzzy logic and ontology. Int J Adv Res Comput Eng Technol (IJARCET) 5(1):165 Vaishali F, Archana G, Monika G, Vidya G, Sanap M (2016) E-learning recommendation system using fuzzy logic and ontology. Int J Adv Res Comput Eng Technol (IJARCET) 5(1):165
23.
Zurück zum Zitat Tarus JK, Niu Z, Kalui D (2018) A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft Comput 22(8):2449–2461CrossRef Tarus JK, Niu Z, Kalui D (2018) A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft Comput 22(8):2449–2461CrossRef
24.
Zurück zum Zitat Wan S, Niu Z (2019) A hybrid e-learning recommendation approach based on learners’ influence propagation. IEEE Trans Knowl Data Eng 32(5):827–840CrossRef Wan S, Niu Z (2019) A hybrid e-learning recommendation approach based on learners’ influence propagation. IEEE Trans Knowl Data Eng 32(5):827–840CrossRef
25.
Zurück zum Zitat Madani Y, Ezzikouri H, Erritali M, Hssina B (2020) Finding optimal pedagogical content in an adaptive e-learning platform using a new recommendation approach and reinforcement learning. J Ambient Intell Humaniz Comput 11(10):3921–3936CrossRef Madani Y, Ezzikouri H, Erritali M, Hssina B (2020) Finding optimal pedagogical content in an adaptive e-learning platform using a new recommendation approach and reinforcement learning. J Ambient Intell Humaniz Comput 11(10):3921–3936CrossRef
28.
Zurück zum Zitat Cai H, Xia R, Yu J (2021) Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol 1: Long Papers), pp 340–350 Cai H, Xia R, Yu J (2021) Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol 1: Long Papers), pp 340–350
29.
Zurück zum Zitat Bu J, Ren L, Zheng S, Yang Y, Wang J, Zhang F, Wu W (2021) ASAP: A Chinese review dataset towards aspect category sentiment analysis and rating prediction. arXiv preprint arXiv:2103.06605 Bu J, Ren L, Zheng S, Yang Y, Wang J, Zhang F, Wu W (2021) ASAP: A Chinese review dataset towards aspect category sentiment analysis and rating prediction. arXiv preprint arXiv:​2103.​06605
30.
Zurück zum Zitat Ke P, Ji H, Liu S, Zhu X, Huang M (2019) SentiLARE: Sentiment-aware language representation learning with linguistic knowledge. arXiv preprint arXiv:1911.02493 Ke P, Ji H, Liu S, Zhu X, Huang M (2019) SentiLARE: Sentiment-aware language representation learning with linguistic knowledge. arXiv preprint arXiv:​1911.​02493
31.
Zurück zum Zitat Sindhu C, Sasmal B, Gupta R, Prathipa J (2021) Subjectivity detection for sentiment analysis on Twitter data. Artificial intelligence techniques for advanced computing applications. Springer, Singapore, pp 467–476CrossRef Sindhu C, Sasmal B, Gupta R, Prathipa J (2021) Subjectivity detection for sentiment analysis on Twitter data. Artificial intelligence techniques for advanced computing applications. Springer, Singapore, pp 467–476CrossRef
32.
Zurück zum Zitat Das N, Sagnika SA (2020) Subjectivity detection-based approach to sentiment analysis. Machine learning and information processing. Springer, Singapore, pp 149–160CrossRef Das N, Sagnika SA (2020) Subjectivity detection-based approach to sentiment analysis. Machine learning and information processing. Springer, Singapore, pp 149–160CrossRef
33.
Zurück zum Zitat Susanto Y, Cambria E, Ng BC, Hussain A (2022) Ten years of sentic computing. Cogn Comput 14(1):5–23CrossRef Susanto Y, Cambria E, Ng BC, Hussain A (2022) Ten years of sentic computing. Cogn Comput 14(1):5–23CrossRef
34.
Zurück zum Zitat Kumar A, Seth S, Gupta S, Maini S (2021) Sentic computing for aspect-based opinion summarization using multi-head attention with feature pooled pointer generator network. Cogn Comput 14:1–19 Kumar A, Seth S, Gupta S, Maini S (2021) Sentic computing for aspect-based opinion summarization using multi-head attention with feature pooled pointer generator network. Cogn Comput 14:1–19
35.
Zurück zum Zitat Pasquier, C., da Costa Pereira, C., & Tettamanzi, A. G. (2020, August) Extending a fuzzy polarity propagation method for multi-domain sentiment analysis with word embedding and pos tagging. In ECAI 2020: 24th European Conference on Artificial Intelligence, August 29-September 8, Santiago de Compostela, Spain (Vol. 325, pp. 2140–2147). IOS Press. Pasquier, C., da Costa Pereira, C., & Tettamanzi, A. G. (2020, August) Extending a fuzzy polarity propagation method for multi-domain sentiment analysis with word embedding and pos tagging. In ECAI 2020: 24th European Conference on Artificial Intelligence, August 29-September 8, Santiago de Compostela, Spain (Vol. 325, pp. 2140–2147). IOS Press.
36.
Zurück zum Zitat Alencar M, Netto J (2020) Measuring student emotions in an online learning environment. In: Proceedings of the 12th International Conference on Agents and Artificial Intelligence, vol 10, p 0008956505630569 Alencar M, Netto J (2020) Measuring student emotions in an online learning environment. In: Proceedings of the 12th International Conference on Agents and Artificial Intelligence, vol 10, p 0008956505630569
39.
Zurück zum Zitat López M, Valdivia A, Martínez-Cámara E, Luzón MV, Herrera F (2019) E2SAM: evolutionary ensemble of sentiment analysis methods for domain adaptation. Inf Sci 480:273–286CrossRef López M, Valdivia A, Martínez-Cámara E, Luzón MV, Herrera F (2019) E2SAM: evolutionary ensemble of sentiment analysis methods for domain adaptation. Inf Sci 480:273–286CrossRef
40.
Zurück zum Zitat El Mekki A, El Mahdaouy A, Berrada I, Khoumsi A (2021) Domain adaptation for Arabic cross-domain and cross-dialect sentiment analysis from contextualized word embedding. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 2824–2837 El Mekki A, El Mahdaouy A, Berrada I, Khoumsi A (2021) Domain adaptation for Arabic cross-domain and cross-dialect sentiment analysis from contextualized word embedding. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 2824–2837
41.
Zurück zum Zitat Gong C, Yu J, Xia R (2020) Unified feature and instance based domain adaptation for end-to-end aspect-based sentiment analysis. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 7035–7045 Gong C, Yu J, Xia R (2020) Unified feature and instance based domain adaptation for end-to-end aspect-based sentiment analysis. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 7035–7045
42.
Zurück zum Zitat Tiwari D, Nagpal B (2021) Ensemble sentiment model: bagging with linear discriminant analysis (BLDA). In: 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, pp 474–480 Tiwari D, Nagpal B (2021) Ensemble sentiment model: bagging with linear discriminant analysis (BLDA). In: 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, pp 474–480
43.
Zurück zum Zitat Cambria E, Li Y, Xing FZ, Poria S, Kwok K (2020) SenticNet 6: ensemble application of symbolic and subsymbolic AI for sentiment analysis. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 105–114 Cambria E, Li Y, Xing FZ, Poria S, Kwok K (2020) SenticNet 6: ensemble application of symbolic and subsymbolic AI for sentiment analysis. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 105–114
44.
Zurück zum Zitat Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:​1301.​3781
45.
Zurück zum Zitat Mikolov TI, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119 Mikolov TI, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119
51.
Zurück zum Zitat Duan R, Jiang C, Jain HK (2022) Combining review-based collaborative filtering and matrix factorization: a solution to rating’s sparsity problem. Decis Support Syst 156:113748CrossRef Duan R, Jiang C, Jain HK (2022) Combining review-based collaborative filtering and matrix factorization: a solution to rating’s sparsity problem. Decis Support Syst 156:113748CrossRef
52.
Zurück zum Zitat Singh M (2020) Scalability and sparsity issues in recommender datasets: a survey. Knowl Inf Syst 62:1–43CrossRef Singh M (2020) Scalability and sparsity issues in recommender datasets: a survey. Knowl Inf Syst 62:1–43CrossRef
53.
Zurück zum Zitat Sarwar B, Karypis G, Konstan J, Riedl J (2000) Application of dimensionality reduction in recommender system—a case study (no. TR-00-043). Minnesota University Minneapolis, Department of Computer Science Sarwar B, Karypis G, Konstan J, Riedl J (2000) Application of dimensionality reduction in recommender system—a case study (no. TR-00-043). Minnesota University Minneapolis, Department of Computer Science
54.
Zurück zum Zitat Kalantzis V, Kollias G, Ubaru S, Nikolakopoulos AN, Horesh L, Clarkson K (2021) Projection techniques to update the truncated SVD of evolving matrices with applications. In: International Conference on Machine Learning, PMLR, pp 5236–5246 Kalantzis V, Kollias G, Ubaru S, Nikolakopoulos AN, Horesh L, Clarkson K (2021) Projection techniques to update the truncated SVD of evolving matrices with applications. In: International Conference on Machine Learning, PMLR, pp 5236–5246
55.
Zurück zum Zitat Koren Y (2009) The bellkor solution to the netflix grand prize. Netflix prize documentation. 81:1–10 Koren Y (2009) The bellkor solution to the netflix grand prize. Netflix prize documentation. 81:1–10
56.
Zurück zum Zitat Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 426–434 Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 426–434
57.
Zurück zum Zitat Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup and workshop, vol. 2007, pp 5–8 Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup and workshop, vol. 2007, pp 5–8
58.
Zurück zum Zitat Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 426–434 Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 426–434
59.
Zurück zum Zitat Sharifi Z, Rezghi M, Nasiri M (2013) New algorithm for recommender systems based on singular value decomposition method. In: ICCKE 2013. IEEE, pp 86–91 Sharifi Z, Rezghi M, Nasiri M (2013) New algorithm for recommender systems based on singular value decomposition method. In: ICCKE 2013. IEEE, pp 86–91
60.
Zurück zum Zitat Yu H-F, Hsieh C-J, Si S, Dhillon I (2012) Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. In: Proceedings of the 2012 IEEE 12th International Conference on Data Mining, Brussels, Belgium, 10–13, pp 765–774 Yu H-F, Hsieh C-J, Si S, Dhillon I (2012) Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. In: Proceedings of the 2012 IEEE 12th International Conference on Data Mining, Brussels, Belgium, 10–13, pp 765–774
64.
Zurück zum Zitat Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451–2471CrossRefPubMed Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451–2471CrossRefPubMed
65.
Zurück zum Zitat Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modelling. arXiv preprint arXiv:1412.3555 Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modelling. arXiv preprint arXiv:​1412.​3555
66.
Zurück zum Zitat Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:​1406.​1078
67.
Zurück zum Zitat Ziegler CN, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversi¯cation. In: Proceedings of the 14th International Conference World Wide Web (WWW '05) (Association for Computing Machinery, New York, NY, USA, 2005), pp 22–32. https://doi.org/10.1145/1060745.1060754 Ziegler CN, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversi¯cation. In: Proceedings of the 14th International Conference World Wide Web (WWW '05) (Association for Computing Machinery, New York, NY, USA, 2005), pp 22–32. https://​doi.​org/​10.​1145/​1060745.​1060754
68.
Zurück zum Zitat Alatrash, R., Priyadarshini, R., Ezaldeen, H., & Alhinnawi, A. (2022b) A Hybrid Recommendation Integrating Semantic Learner Modelling and Sentiment Multi-Classification. Journal of Web Engineering, 941–988. Alatrash, R., Priyadarshini, R., Ezaldeen, H., & Alhinnawi, A. (2022b) A Hybrid Recommendation Integrating Semantic Learner Modelling and Sentiment Multi-Classification. Journal of Web Engineering, 941–988.
70.
Zurück zum Zitat Anwar T, Uma V, Srivastava G (2021) Rec-cfsvd++: implementing recommendation system using collaborative filtering and singular value decomposition (svd)++. Int J Inf Technol Decis Mak 20(04):1075–1093CrossRef Anwar T, Uma V, Srivastava G (2021) Rec-cfsvd++: implementing recommendation system using collaborative filtering and singular value decomposition (svd)++. Int J Inf Technol Decis Mak 20(04):1075–1093CrossRef
71.
Zurück zum Zitat Hamada M, Odu NB, Hassan M (2018) A fuzzy-based approach for modelling preferences of users in multi-criteria recommender systems. In: 2018 IEEE 12th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC). IEEE, pp 87–94 Hamada M, Odu NB, Hassan M (2018) A fuzzy-based approach for modelling preferences of users in multi-criteria recommender systems. In: 2018 IEEE 12th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC). IEEE, pp 87–94
74.
Zurück zum Zitat Jannach D, Lerche L, Zanker M (2018) Recommending based on implicit feedback. Social information access: systems and technologies. Springer, Cham, pp 510–569CrossRef Jannach D, Lerche L, Zanker M (2018) Recommending based on implicit feedback. Social information access: systems and technologies. Springer, Cham, pp 510–569CrossRef
Metadaten
Titel
Collaborative filtering integrated fine-grained sentiment for hybrid recommender system
verfasst von
Rawaa Alatrash
Rojalina Priyadarshini
Hadi Ezaldeen
Publikationsdatum
21.09.2023
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 4/2024
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-023-05600-w