Improving Collaborative Filter Using BERT
Keywords:Recommendation systems, Semantic similarity , Collaborative filter, BERT, K-Nearest neighbor
With the increasing number of books published and the difficulty of obtaining appropriate research attention, the recommendation systems can increase the affordability and availability of these books. In this work, we expand our work to enhance the accuracy of book collaborative filtering by applying semantic similarity to book summaries, in addition to that addressing major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of three stages: preprocessing, building the system, and evaluation. The technologies used in the pre-processing stage included reduction and normalization. The construction system is divided into two phases: semantic similarity and recommendation. The semantic similarity is done by using BERT for sentence embedding and cosine similarity to calculate the similarity between sentences. During the recommendation phase by using CF based on KNN. In the evaluation stage, classification accuracy metrics had been used. The proposed approach improved the accuracy of the book recommendation system and increased the accuracy to 0.89 compared to previous works on a dataset of 271,000 book summaries. The proposed approach yielded better results due to avoiding problems in previous work, such as scalability and sparsity, by using BERT with CF based KNN. Filtering the data using BERT and the KNN algorithm in the CF added strength to the recommendation, which led to an increase in the accuracy rate.
Zhu, Yifan, et al. "Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks." Knowledge-Based Systems 215 (2021): 106744.
Nassar, Nour, Assef Jafar, and Yasser Rahhal. "A novel deep multi-criteria collaborative filtering model for recommendation system." Knowledge-Based Systems 187 (2020): 104811.
Jannach, D., Manzoor, A., Cai, W., & Chen, L. "A survey on conversational recommender systems." ACM Computing Surveys (CSUR) 54.5 (2021): 1-36.
[-3] Hui, B., Zhang, L., Zhou, X., Wen, X., & Nian, Y. "Personalized recommendation system based on knowledge embedding and historical behavior." Applied Intelligence (2022): 1-13.
Sallam, Rouhia Mohammed, Mahmoud Hussein, and Hamdy M. Mousa. "Improving collaborative filtering using lexicon-based sentiment analysis." International Journal of Electrical and Computer Engineering 12.2 (2022): 1744.
Geng, Li. "The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model." Computational Intelligence and Neuroscience 2022 (2022).
Fkih, Fethi. "Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison." Journal of King Saud University-Computer and Information Sciences 34.9 (2022): 7645-7669.
Zhou, Xin, and Wenan Tan. "An Improved Collaborative Filtering Algorithm Based on Filling Missing Data." Human Centered Computing: 6th International Conference, HCC 2020, Virtual Event, December 14–15, 2020, Revised Selected Papers 6. Springer International Publishing, 2021.
Alhijawi, Bushra, and Yousef Kilani. "A collaborative filtering recommender system using genetic algorithm." Information Processing & Management 57.6 (2020): 102310.
Sakib, Nazmus, Rodina Binti Ahmad, and Khalid Haruna. "A collaborative approach toward scientific paper recommendation using citation context." IEEE Access 8 (2020): 51246-51255.
Neysiani, B. S., Soltani, N., Mofidi, R., & Nadimi-Shahraki, M. H. "Improve performance of association rule-based collaborative filtering recommendation systems using genetic algorithm." International Journal of Information Technology and Computer Science 11.2 (2019): 48-55.
Liu, Gaojun, and Xingyu Wu. "Using collaborative filtering algorithms combined with Doc2Vec for movie recommendation." 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2019.
Dubey, A., Gupta, A., Raturi, N., & Saxena, P. "Item-based collaborative filtering using sentiment analysis of user reviews." International Conference on Application of Computing and Communication Technologies. Springer, Singapore, 2018.
Haruna, K., Akmar Ismail, M., Damiasih, D., Sutopo, J., & Herawan, T. "A collaborative approach for research paper recommender system." PloS one 12.10 (2017): e0184516.
Rajasundari, T., P. Subathra, and P. N. Kumar. "Performance analysis of topic modeling algorithms for news articles." Journal of Advanced Research in Dynamical and Control Systems 11 (2017): 175-183.
Al-augby, Salam, and Kesra Nermend. "USING RULE TEXT MINING BASED ALGORITHM TO SUPPORT THE STOCK MARKET INVESTMENT DECISION." Transformations in Business & Economics 14 (2015).
Tong, Zhou, and Haiyi Zhang. "A text mining research based on LDA topic modelling." International conference on computer science, engineering and information technology. 2016.
N. Adaloglou, “Transformers in Computer Vision,” https://theaisummer.com/, 2021.
C. McCormick and N. Ryan, “BERT Word Embeddings Tutorial,” 2019, [Online]. Available: http://www.mccormickml.com.
Park, Kwangil, June Seok Hong, and Wooju Kim. "A methodology combining cosine similarity with classifier for text classification." Applied Artificial Intelligence 34.5 (2020): 396-411.
Gasmi, Sara, Tahar Bouhadada, and Abdelmadjid Benmachiche. "Survey on Recommendation Systems." Proceedings of the 10th International Conference on Information Systems and Technologies. (2020).
Bai, Xiaomei, et al. "Scientific paper recommendation: A survey." Ieee Access 7 (2019): 9324-9339.
Madia, Nidhi, Amit Thakkar, and Kamlesh Makvana. "Survey on recommendation system using semantic web mining." International Journal of Innovative and Emerging Research in Engineering 2.2 (2015).
Gupta, M., Thakkar, A., Gupta, V., & Rathore, D. P. S. "Movie recommender system using collaborative filtering." 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2020.
Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. "Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction." Scientific Reports 12.1 (2022): 1-11.
Chicco, Davide, and Giuseppe Jurman. "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation." BMC genomics 21 (2020): 1-13.
Jumadi, J., Maylawati, D. S., Pratiwi, L. D., & Ramdhani, M. A. "Comparison of Nazief-Adriani and Paice-Husk algorithm for Indonesian text stemming process." IOP Conference Series: Materials Science and Engineering. Vol. 1098. No. 3. IOP Publishing, 2021.
How to Cite
Copyright (c) 2023 Riyam Rwedhi, Salam Al-augby
This work is licensed under a Creative Commons Attribution 4.0 International License.