Recommendation systems: A Review
Sari
Recommendation systems have become one of the most widespread types of systems today, as they have become a necessity and a need. Recommendation systems can be defined as methods for presenting or marketing electronic products (in various forms) to users, with the selection of products based on the user's actual needs. This is achieved through the use of specific algorithms and methods to gauge the user's interest in the suggested products. Recently, the applications of recommendation systems have expanded beyond a single field or aspect, extending into many areas of life and science. Many methods have been developed and improved for building recommendation systems from naive to advanced ones.
This study aims to provide a comprehensive overview of recommendation systems: definitions, objectives, and types, including the advantages and disadvantages of each type.
Kata Kunci
Teks Lengkap:
PDF ()Referensi
Abdul, Wajeed & Adilakshmi, T.. (2011). Different similarity measures for text classification using KNN. 2011 2nd International Conference on Computer and Communication Technology, ICCCT-2011. 41-45. 10.1109/ICCCT.2011.6075188.
Adomavicius, Gediminas & Mobasher, Bamshad & Ricci, Francesco & Tuzhilin, Alexander. (2011). Context-Aware Recommender Systems. AI Magazine. 32. 67-80. 10.1609/aimag.v32i3.2364.
Al Bakri, Nadia & Hashem, Soukaena. (2019). Collaborative Filtering Recommendation Model Based on k-means Clustering. Al-Nahrain Journal of Science. 22. 74-79. 10.22401/ANJS.22.1.10.
Al-Ansari, Khaled. (2020). Survey on Word Embedding Techniques in Natural Language Processing.
Amini, Monireh & Nasiri, Mahdi & Afzali, Mehdi. (2014). Proposing a New Hybrid Approach in Movie Recommender System. International Journal of Computer Science and Information Security. 12.
Andrabi, Syed & Wahid, Abdul. (2022). A Comparative Study of Word Embedding Techniques in Natural Language Processing. 10.1007/978-981-16-9573-5_50.
Angadi, Anupama & Gorripati, Satya & Varma, P.. (2018). Temporal Community-Based Collaborative Filtering to Relieve from Cold-Start and Sparsity Problems. International Journal of Intelligent Systems and Applications. 10. 53-62. 10.5815/ijisa.2018.10.06.
Azarijafari, Mohammad. (2021). A Survey on Word Embedding Techniques in Text Processing.
Basak, Sayani & Chowdhury, Sneha & Goswami, Soham & Gayen, Tousik & Pandey, Shatakshi & Sarkar, Rupanwita. (2023). Movie Recommendation Using Hybrid-Based Approach. American Journal of Electronics & Communication. 4. 12-18. 10.15864/ajec.4203.
Berisha, Fjolla & Bytyçi, Eliot. (2023). Addressing cold start in recommender systems with neural networks: a literature survey. International Journal of Computers and Applications. 45. 1-12. 10.1080/1206212X.2023.2237766.
Cui, Bei-Bei. (2017). Design and Implementation of Movie Recommendation System Based on Knn Collaborative Filtering Algorithm. ITM Web of Conferences. 12. 04008. 10.1051/itmconf/20171204008.
Gope, Jyotirmoy & Jain, Sanjay. (2017). A survey on solving cold start problem in recommender systems. 133-138. 10.1109/CCAA.2017.8229786.
Gomaa, Wael & Fahmy, Aly. (2013). A Survey of Text Similarity Approaches. International Journal of Computer Applications. 68. 10.5120/11638-7118.
Gorripati, S.K., & Vatsavayi, V.K. (2017). Community-Based Collaborative Filtering to Alleviate the Cold-Start and Sparsity Problems.
Gunasekar, Geetha & Iqubal, Safa & Chelladurai, Fancy & Saranya, D. (2018). A Hybrid Approach using Collaborative Filtering and Content-based Filtering for Recommender System. Journal of Physics: Conference Series. 1000. 012101. 10.1088/1742-6596/1000/1/012101
Haruna, Khalid & Ismail, Maizatul Akmar & Damiasih, Damiasih & Sutopo, Joko & Herawan, Tutut. (2017). A collaborative approach for research paper recommender system. PLOS ONE. 12. e0184516. 10.1371/journal.pone.0184516.
Hasan, Mahamudul & Roy, Falguni. (2019). An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem. Big Data and Cognitive Computing. 3. 39. 10.3390/bdcc3030039.
Huang, Cheng-Hui & Yin, Jian & Hou, Fang. (2011). A Text Similarity Measurement Combining Word Semantic Information with TF-IDF Method. Chinese Journal of Computers. 34. 856-864. 10.3724/SP.J.1016.2011.00856.
Javed, Umair & Shaukat, Kamran & Hameed, Ibrahim & Iqbal, Farhat & Mahboob Alam, Talha & Luo, Suhuai. (2021). A Review of Content-Based and Context-Based Recommendation Systems. International Journal of Emerging Technologies in Learning (iJET). 16. 10.3991/ijet.v16i03.18851.
Keshava, M. & Reddy, P. & Srinivasulu, S. & Naik, B.. (2020). Machine Learning Model for Movie Recommendation System. International Journal of Engineering Research and. V9. 10.17577/IJERTV9IS040741.
Klimashevskaia, Anastasiia & Jannach, Dietmar & Elahi, Mehdi & Trattner, Christoph. (2023). A Survey on Popularity Bias in Recommender Systems. 10.48550/arXiv.2308.01118.
Koren, Y., Bell, R., Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems.
Lam, X.N., Vu, T., Le, T.D., & Duong, A.D. (2008). Addressing cold-start problem in recommendation systems. International Conference on Ubiquitous Information Management and Communication.
Lan, Fei. (2022). Research on Text Similarity Measurement Hybrid Algorithm with Term Semantic Information and TF-IDF Method. Advances in Multimedia. 2022. 1-11. 10.1155/2022/7923262.
Li, Qing & Kim, Byeong. (2003). An approach for combining content-based and collaborative filters. 17-24. 10.3115/1118935.1118938.
Liu, Haifeng & Hu, Zheng & Mian, Ahmad & Tian, Hui & Zhu, Xuzhen. (2014). A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems. 56. 156–166. 10.1016/j.knosys.2013.11.006.
Lops, Pasquale & de Gemmis, Marco & Semeraro, Giovanni. (2011). Content-based Recommender Systems: State of the Art and Trends. 10.1007/978-0-387-85820-3_3.
lu, Yu & Yu, Shoou-I & Chang, Tsung-Chieh & Hsu, Jane. (2009). A Content-Based Method to Enhance Tag Recommendation. 2064-2069.
Maake, Benard & Zuva, Tranos. (2018). A Comparative Analysis of Text Similarity Measures and Algorithms in Research Paper Recommender Systems. 10.1109/ICTAS.2018.8368766.
Mohd, Abdul & Hameed, Mohd & al Jadaan, Omar & Sirandas, Ramachandram. (2012). Collaborative Filtering Based Recommendation System: A survey. International Journal on Computer Science and Engineering. 4.
Musa, Aminu & Haruna, Khalid & Ibrahim Jibia, Fa'Iz & Yunusa, Zayyanu & Ibrahim, Yakubu. (2021). Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes. SINTECH (Science and Information Technology) Journal. 2. 10.31763/sitech.v2i1.610.
Nguyen, Van-Doan & Sriboonchitta, Songsak & Huynh, Van-Nam. (2017). Using Community Preference for Overcoming Sparsity and Cold-Start Problems in Collaborative Filtering System Offering Soft Ratings. Electronic Commerce Research and Applications. 26. 101-108. 10.1016/j.elerap.2017.10.002.
Panda, Deepak & Ray, Sanjog. (2022). Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review. Journal of Intelligent Information Systems. 59. 1-26. 10.1007/s10844-022-00698-5.
Papadakis, Harris & Papagrigoriou, Antonis & Kosmas, Eleftherios & Panagiotakis, Costas & Markaki, Smaragda & Fragopoulou, Paraskevi. (2023). Content-Based Recommender Systems Taxonomy. Foundations of Computing and Decision Sciences. 48. 211-241. 10.2478/fcds-2023-0009.
Polatidis, Nikolaos & Georgiadis, Christos. (2015). A multi-level collaborative filtering method that improves recommendations. Expert Systems with Applications. 48. 10.1016/j.eswa.2015.11.023.
Prando, Alan & Contratres, Felipe & Souza, Solange N A & Souza, Luiz. (2017). Content-based Recommender System using Social Networks for Cold-start Users. 181-189. 10.5220/0006496301810189.
Rahman, Md. Mijanur & Shama, Ismat & Rahman, Siamur & Nabil, Rahmatullah. (2022). Hybrid Recommendation System to Solve Cold Start Problem. Journal of Theoretical and Applied Information Technology. 100.
Reshak, Kaiser & Dhannoon, Ban & Sultani, Zainab. (2023). Hybrid recommender system based on matrix factorization. AIP Conference Proceedings. 2457. 40010. 10.1063/5.0118335.
Salmani, Sakina & Kulkarni, Sarvesh. (2021). Hybrid Movie Recommendation System Using Machine Learning. 1-10. 10.1109/ICCICT50803.2021.9510058.
Sarwar, Badrul & Badrul, & Karypis, George & Cybenko, George & Konstan, & Joseph, & Reidl, & Tsibouklis, John. (2001). Item-based collaborative filtering recommendation algorithmus.
Shen, Jian. (2013). Collaborative Filtering Recommendation Algorithm Based on Two Stages of Similarity Learning and Its Optimization. IFAC Proceedings Volumes. 46. 335-340. 10.3182/20130708-3-CN-2036.00068.
Son, Jieun & Kim, Sb. (2017). Content-Based Filtering for Recommendation Systems Using Multiattribute Networks. Expert Systems with Applications. 89. 10.1016/j.eswa.2017.08.008.
Son, L.H. (2016). Dealing with the new user cold-start problem in recommender systems: A comparative review. Inf. Syst., 58, 87-104.
Volkovs, Maksims & Yu, Guang Wei & Poutanen, Tomi. (2017). Content-based Neighbor Models for Cold Start in Recommender Systems. 1-6. 10.1145/3124791.3124792.
Wang, Donghui & Liang, Yanchun & Xu, Dong & Feng, Xiaoyue & Guan, Renchu. (2018). A Content-Based Recommender System for Computer Science Publications. Knowledge-Based Systems. 157. 10.1016/j.knosys.2018.05.001.
Yuan, Hongli & Hernandez, Alexander. (2023). User Cold Start Problem in Recommendation Systems: A Systematic Review. 10.1109/ACCESS.2023.3338705.
DOI: https://doi.org/10.33387/ijeeic.v2i2.9956
Refbacks
- Saat ini tidak ada refbacks.
##submission.license.cc.by-nc-sa4.footer##