Issue
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Movie Recommender System on Twitter Using Weighted Hybrid Filtering and GRU
Corresponding Author(s) : Nico Valentino
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 9, No. 2, May 2024
Abstract
The development of the industry in the film sector has experienced rapid growth, marked by the emergence of film streaming platforms such as Netflix and Disney+. With the abundance of available films, users face difficulty in choosing films that suit their preferences. Recommender systems can be a solution to this problem for users. Recommender systems rely on user reviews, making Twitter a platform that can be used to collect user reviews of a film. This study will develop a recommender system that has the potential to provide item recommendations to users using the weighted hybrid filtering and GRU methods. The weighted hybrid filtering used is a combination of collaborative filtering and content-based filtering methods. The dataset used in this study was obtained by crawling tweets relevant to the feedback of specific accounts regarding a film. The dataset resulting from the data crawling consists of a total of 854 films, 45 users and 34,086 tweets consisting of film reviews from Twitter users. The GRU model classification is performed on the results of weighted hybrid filtering with model optimization involving testing various test size scenarios and optimizer methods. The test sizes used are 40%, 30%, and 20%. The optimizer methods used include Adam, Nadam, Adamax, Adadelta, Adagrad, and SGD. The research results show that the optimal outcome is obtained using the Nadam optimization method. The performance evaluation yielded 85.74% precision, 88.63% recall, 88.63% accuracy, and 86.30% F1-score.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- K. Sailunaz and R. Alhajj, “Emotion and sentiment analysis from Twitter text,” J. Comput. Sci., vol. 36, p. 101003, 2019, doi: https://doi.org/10.1016/j.jocs.2019.05.009.
- H. Tahmasebi, R. Ravanmehr, and R. Mohamadrezaei, “Social movie recommender system based on deep autoencoder network using Twitter data,” Neural Comput. Appl., vol. 33, no. 5, pp. 1607–1623, 2021, doi: https://doi.org/10.1007/s00521-020-05085-1.
- G. Geetha, M. Safa, C. Fancy, and D. Saranya, “A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System,” J. Phys. Conf. Ser., vol. 1000, no. 1, 2018, doi: https://doi.org/10.1088/1742-6596/1000/1/012101.
- N. Ifada, T. F. Rahman, and M. K. Sophan, “Comparing collaborative filtering and hybrid based approaches for movie recommendation,” Proceeding - 6th Inf. Technol. Int. Semin. ITIS 2020, pp. 219–223, 2020, doi: https://doi.org/10.1109/ITIS50118.2020.9321014.
- S. Natarajan, S. Vairavasundaram, S. Natarajan, and A. H. Gandomi, “Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data,” Expert Syst. Appl., vol. 149, 2020, doi: https://doi.org/10.1016/j.eswa.2020.113248.
- S. Ahmadian, M. Afsharchi, and M. Meghdadi, “A novel approach based on multi-view reliability measures to alleviate data sparsity in recommender systems,” Multimed. Tools Appl., vol. 78, no. 13, pp. 17763–17798, 2019, doi: https://doi.org/10.1007/s11042-018-7079-x.
- R. Logesh and V. Subramaniyaswamy, Exploring hybrid recommender systems for personalized travel applications, vol. 768. Springer Singapore, 2019, doi: https://doi.org/10.1007/978-981-13-0617-4_52.
- H. Q. Do, T. H. Le, and B. Yoon, “Dynamic Weighted Hybrid Recommender Systems,” Int. Conf. Adv. Commun. Technol. ICACT, vol. 2020, pp. 644–650, 2020, doi: https://doi.org/10.23919/ICACT48636.2020.9061465.
- M. Gupta, A. Thakkar, Aashish, V. Gupta, and D. P. S. R. Rathore, “Movie Recommender System Using Collaborative Filtering,” no. Icesc, pp. 415–420, 2020, doi: https://doi.org/10.1109/ICESC48915.2020.9155879.
- Y. Fu and T. Wang, “Item-based collaborative filtering with BERT,” Proc. Annu. Meet. Assoc. Comput. Linguist., vol. 2020-July, no. Ecnlp 3, pp. 54–58, 2020, doi: https://doi.org/10.18653/v1/2020.ecnlp-1.8.
- X. Wang, Z. Dai, H. Li, and J. Yang, “A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning,” Discret. Dyn. Nat. Soc., vol. 2020, no. 1, 2020, doi: https://doi.org/10.1155/2020/6480273.
- S. Wan and Z. Niu, “A hybrid e-learning recommendation approach based on learners’ influence propagation,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 5, pp. 827–840, 2020, doi: https://doi.org/10.1109/TKDE.2019.2895033.
- J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” pp. 1–9, 2014. https://doi.org/10.48550/arXiv.1412.3555
- K. E. ArunKumar, D. V. Kalaga, C. M. S. Kumar, M. Kawaji, and T. M. Brenza, “Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells,” Chaos, Solitons and Fractals, vol. 146, p. 110861, 2021, doi: https://doi.org/10.1016/j.chaos.2021.110861.
- D. Valcarce, A. Landin, J. Parapar, and Á. Barreiro, “Collaborative filtering embeddings for memory-based recommender systems,” Eng. Appl. Artif. Intell., vol. 85, no. May, pp. 347–356, 2019, doi: https://doi.org/10.1016/j.engappai.2019.06.020.
- G. R. Lima, C. E. Mello, A. Lyra, and G. Zimbrao, “Applying landmarks to enhance memory-based collaborative filtering,” Inf. Sci. (Ny)., vol. 513, pp. 412–428, 2020, doi: https://doi.org/10.1016/j.ins.2019.10.041.
- C. Ajaegbu, “An optimized item-based collaborative filtering algorithm,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 12, pp. 10629–10636, 2021, doi: https://doi.org/10.1007/s12652-020-02876-1.
- A. S. Tewari, “Generating Items Recommendations by Fusing Content and User-Item based Collaborative Filtering,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 1934–1940, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.215.
- Y. Afoudi, M. Lazaar, and M. Al Achhab, “Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network,” Simul. Model. Pract. Theory, vol. 113, no. June, p. 102375, 2021, doi: https://doi.org/10.1016/j.simpat.2021.102375.
- D. Wang, Y. Liang, D. Xu, X. Feng, and R. Guan, “A content-based recommender system for computer science publications,” Knowledge-Based Syst., vol. 157, pp. 1–9, 2018, doi: https://doi.org/10.1016/j.knosys.2018.05.001.
- M. Abdel-Basset, M. Mohamed, M. Elhoseny, L. H. Son, F. Chiclana, and A. E. N. H. Zaied, “Cosine similarity measures of bipolar neutrosophic set for diagnosis of bipolar disorder diseases,” Artif. Intell. Med., vol. 101, p. 101735, 2019, doi: https://doi.org/10.1016/j.artmed.2019.101735.
- R. H. Singh, S. Maurya, T. Tripathi, T. Narula, and G. Srivastav, “Movie Recommendation System using Cosine Similarity and KNN,” Int. J. Eng. Adv. Technol., vol. 9, no. 5, pp. 556–559, 2020, doi: https://doi.org/10.35940/ijeat.e9666.069520.
- R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, “A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction,” J. Appl. Sci. Technol. Trends, vol. 1, no. 2, pp. 56–70, 2020, doi: https://doi.org/10.38094/jastt1224.
- S. Hakak, M. Alazab, S. Khan, T. R. Gadekallu, P. K. R. Maddikunta, and W. Z. Khan, “An ensemble machine learning approach through effective feature extraction to classify fake news,” Futur. Gener. Comput. Syst., vol. 117, pp. 47–58, 2021, doi: https://doi.org/10.1016/j.future.2020.11.022.
- S. W. Kim and J. M. Gil, “Research paper classification systems based on TF-IDF and LDA schemes,” Human-centric Comput. Inf. Sci., vol. 9, no. 1, 2019, doi: https://doi.org/10.1186/s13673-019-0192-7.
- J. M. Kudari, “Fake News Detection using Passive Aggressive and TF-IDF Vectorizer,” Int. Res. J. Eng. Technol., pp. 1601–1603, 2020.
- S. Pericherla and E. Ilavarasan, “Performance analysis of Word Embeddings for Cyberbullying Detection,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1085, no. 1, p. 012008, 2021. https://doi.org/10.1088/1757-899x/1085/1/012008
- T. Schick and H. Schütze, “BERTRAM: Improved word embeddings have big impact on contextualized model performance,” Proc. Annu. Meet. Assoc. Comput. Linguist., pp. 3996–4007, 2020, doi: https://doi.org/10.18653/v1/2020.acl-main.368.
References
K. Sailunaz and R. Alhajj, “Emotion and sentiment analysis from Twitter text,” J. Comput. Sci., vol. 36, p. 101003, 2019, doi: https://doi.org/10.1016/j.jocs.2019.05.009.
H. Tahmasebi, R. Ravanmehr, and R. Mohamadrezaei, “Social movie recommender system based on deep autoencoder network using Twitter data,” Neural Comput. Appl., vol. 33, no. 5, pp. 1607–1623, 2021, doi: https://doi.org/10.1007/s00521-020-05085-1.
G. Geetha, M. Safa, C. Fancy, and D. Saranya, “A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System,” J. Phys. Conf. Ser., vol. 1000, no. 1, 2018, doi: https://doi.org/10.1088/1742-6596/1000/1/012101.
N. Ifada, T. F. Rahman, and M. K. Sophan, “Comparing collaborative filtering and hybrid based approaches for movie recommendation,” Proceeding - 6th Inf. Technol. Int. Semin. ITIS 2020, pp. 219–223, 2020, doi: https://doi.org/10.1109/ITIS50118.2020.9321014.
S. Natarajan, S. Vairavasundaram, S. Natarajan, and A. H. Gandomi, “Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data,” Expert Syst. Appl., vol. 149, 2020, doi: https://doi.org/10.1016/j.eswa.2020.113248.
S. Ahmadian, M. Afsharchi, and M. Meghdadi, “A novel approach based on multi-view reliability measures to alleviate data sparsity in recommender systems,” Multimed. Tools Appl., vol. 78, no. 13, pp. 17763–17798, 2019, doi: https://doi.org/10.1007/s11042-018-7079-x.
R. Logesh and V. Subramaniyaswamy, Exploring hybrid recommender systems for personalized travel applications, vol. 768. Springer Singapore, 2019, doi: https://doi.org/10.1007/978-981-13-0617-4_52.
H. Q. Do, T. H. Le, and B. Yoon, “Dynamic Weighted Hybrid Recommender Systems,” Int. Conf. Adv. Commun. Technol. ICACT, vol. 2020, pp. 644–650, 2020, doi: https://doi.org/10.23919/ICACT48636.2020.9061465.
M. Gupta, A. Thakkar, Aashish, V. Gupta, and D. P. S. R. Rathore, “Movie Recommender System Using Collaborative Filtering,” no. Icesc, pp. 415–420, 2020, doi: https://doi.org/10.1109/ICESC48915.2020.9155879.
Y. Fu and T. Wang, “Item-based collaborative filtering with BERT,” Proc. Annu. Meet. Assoc. Comput. Linguist., vol. 2020-July, no. Ecnlp 3, pp. 54–58, 2020, doi: https://doi.org/10.18653/v1/2020.ecnlp-1.8.
X. Wang, Z. Dai, H. Li, and J. Yang, “A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning,” Discret. Dyn. Nat. Soc., vol. 2020, no. 1, 2020, doi: https://doi.org/10.1155/2020/6480273.
S. Wan and Z. Niu, “A hybrid e-learning recommendation approach based on learners’ influence propagation,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 5, pp. 827–840, 2020, doi: https://doi.org/10.1109/TKDE.2019.2895033.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” pp. 1–9, 2014. https://doi.org/10.48550/arXiv.1412.3555
K. E. ArunKumar, D. V. Kalaga, C. M. S. Kumar, M. Kawaji, and T. M. Brenza, “Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells,” Chaos, Solitons and Fractals, vol. 146, p. 110861, 2021, doi: https://doi.org/10.1016/j.chaos.2021.110861.
D. Valcarce, A. Landin, J. Parapar, and Á. Barreiro, “Collaborative filtering embeddings for memory-based recommender systems,” Eng. Appl. Artif. Intell., vol. 85, no. May, pp. 347–356, 2019, doi: https://doi.org/10.1016/j.engappai.2019.06.020.
G. R. Lima, C. E. Mello, A. Lyra, and G. Zimbrao, “Applying landmarks to enhance memory-based collaborative filtering,” Inf. Sci. (Ny)., vol. 513, pp. 412–428, 2020, doi: https://doi.org/10.1016/j.ins.2019.10.041.
C. Ajaegbu, “An optimized item-based collaborative filtering algorithm,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 12, pp. 10629–10636, 2021, doi: https://doi.org/10.1007/s12652-020-02876-1.
A. S. Tewari, “Generating Items Recommendations by Fusing Content and User-Item based Collaborative Filtering,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 1934–1940, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.215.
Y. Afoudi, M. Lazaar, and M. Al Achhab, “Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network,” Simul. Model. Pract. Theory, vol. 113, no. June, p. 102375, 2021, doi: https://doi.org/10.1016/j.simpat.2021.102375.
D. Wang, Y. Liang, D. Xu, X. Feng, and R. Guan, “A content-based recommender system for computer science publications,” Knowledge-Based Syst., vol. 157, pp. 1–9, 2018, doi: https://doi.org/10.1016/j.knosys.2018.05.001.
M. Abdel-Basset, M. Mohamed, M. Elhoseny, L. H. Son, F. Chiclana, and A. E. N. H. Zaied, “Cosine similarity measures of bipolar neutrosophic set for diagnosis of bipolar disorder diseases,” Artif. Intell. Med., vol. 101, p. 101735, 2019, doi: https://doi.org/10.1016/j.artmed.2019.101735.
R. H. Singh, S. Maurya, T. Tripathi, T. Narula, and G. Srivastav, “Movie Recommendation System using Cosine Similarity and KNN,” Int. J. Eng. Adv. Technol., vol. 9, no. 5, pp. 556–559, 2020, doi: https://doi.org/10.35940/ijeat.e9666.069520.
R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, “A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction,” J. Appl. Sci. Technol. Trends, vol. 1, no. 2, pp. 56–70, 2020, doi: https://doi.org/10.38094/jastt1224.
S. Hakak, M. Alazab, S. Khan, T. R. Gadekallu, P. K. R. Maddikunta, and W. Z. Khan, “An ensemble machine learning approach through effective feature extraction to classify fake news,” Futur. Gener. Comput. Syst., vol. 117, pp. 47–58, 2021, doi: https://doi.org/10.1016/j.future.2020.11.022.
S. W. Kim and J. M. Gil, “Research paper classification systems based on TF-IDF and LDA schemes,” Human-centric Comput. Inf. Sci., vol. 9, no. 1, 2019, doi: https://doi.org/10.1186/s13673-019-0192-7.
J. M. Kudari, “Fake News Detection using Passive Aggressive and TF-IDF Vectorizer,” Int. Res. J. Eng. Technol., pp. 1601–1603, 2020.
S. Pericherla and E. Ilavarasan, “Performance analysis of Word Embeddings for Cyberbullying Detection,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1085, no. 1, p. 012008, 2021. https://doi.org/10.1088/1757-899x/1085/1/012008
T. Schick and H. Schütze, “BERTRAM: Improved word embeddings have big impact on contextualized model performance,” Proc. Annu. Meet. Assoc. Comput. Linguist., pp. 3996–4007, 2020, doi: https://doi.org/10.18653/v1/2020.acl-main.368.