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Journal articles on the topic 'RECOMMENDATION TECHNIQUES'

Author: Grafiati

Published: 11 September 2023

Last updated: 26 July 2025

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1

Dondekar, Anupama D., and Balwant A. Sonkamble. "Tag Recommendation Techniques for Images: A Survey." International Journal of Signal Processing Systems 5, no. 4 (2017): 116–22. http://dx.doi.org/10.18178/ijsps.5.4.116-122.

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Gorli, Ravi, and Bagusetty Ajay Ram. "MRML-Movie Recommendation Model with Machine Learning Techniques." International Journal of Science and Research (IJSR) 12, no. 5 (2023): 298–302. http://dx.doi.org/10.21275/sr23322101301.

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Tewari, Anand Shanker, and Asim Gopal Barman. "Sequencing of items in personalized recommendations using multiple recommendation techniques." Expert Systems with Applications 97 (May 2018): 70–82. http://dx.doi.org/10.1016/j.eswa.2017.12.019.

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TR, Mahesh, and V Vinoth Kumar. "Clustering Techniques for Recommendation of Movies." International Journal of Data Informatics and Intelligent Computing 1, no. 2 (2022): 16–22. http://dx.doi.org/10.59461/ijdiic.v1i2.17.

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A recommendation system employs a variety of algorithms to provide users with recommendations of any kind. The most well-known technique, collaborative filtering, involves users with similar preferences although it is not always as effective when dealing with large amounts of data. Improvements to this approach are required as the dataset size increases. Here, in our suggested method, we combine a hierarchical clustering methodology with a collaborative filtering algorithm for making recommendations. Additionally, the Principle Component Analysis (PCA) method is used to condense the dimensions

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El-Deeb, Reham Hesham, Walid Abdelmoez, and Nashwa El-Bendary. "Enhancing E-Recruitment Recommendations Through Text Summarization Techniques." Information 16, no. 4 (2025): 333. https://doi.org/10.3390/info16040333.

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This research aims to enhance e-recruitment systems using text summarization techniques and pretrained large language models (LLMs). A job recommender system is built with integrated text summarization. The text summarization techniques that are selected are BART, T5 (Text-to-Text Transfer Transformer), BERT, and Pegasus. Content-based recommendation is the model chosen to be implemented. The LinkedIn Job Postings dataset is used. The evaluation of the text summarization techniques is performed using ROUGE-1, ROUGE-2, and ROUGE-L. The results of this approach deduce that the recommendation doe

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Idakwo, John, Joshua Babatunde Agbogun, and Taiwo Kolajo. "A Survey on Recommendation System Techniques." UMYU Scientifica 2, no. 2 (2023): 112–19. http://dx.doi.org/10.56919/usci.2322.012.

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The primary objective of recommender systems (RS) is to analyze user behavior and propose relevant items or services that users would find appealing. Recommender systems have gained significant prominence in various domains such as information technology and e-commerce. They achieve this by customizing recommendations based on individual preferences, efficiently filtering options from a vast pool, and enabling users to discover content that matches their interests. Numerous recommendation techniques have been developed to generate personalized suggestions, including collaborative filtering, co

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Al-Absi, Mohammed Abdulhakim, and Hind R’bigui. "Process Discovery Techniques Recommendation Framework." Electronics 12, no. 14 (2023): 3108. http://dx.doi.org/10.3390/electronics12143108.

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In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting process related knowledge from the information recorded in today’s process aware information systems such as ‘Enterprise Resource Planning’ systems, ‘Business Process Management’ systems, ‘Supply Chain Management’ systems, etc. One of the major categories of process mining techniques is the process of discovery. Th

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Gaurkhede, Miss Pratiksha P. "Review Paper on various Recommendation Techniques of Friends Recommendation System." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (2021): 894–97. http://dx.doi.org/10.22214/ijraset.2021.33770.

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Chandrasekaran, K. S., G. A. Varun, D. S. Sujjit, H. Subashree, and R. Thirumaalchelvan. "Movie Recommendation System Using Machine Learning Techniques." International Journal of Multidisciplinary Research Transactions 5, no. 7 (2023): 148–57. https://doi.org/10.5281/zenodo.7942135.

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Movie recommendation systems are becoming increasingly popular, with many businesses looking to leverage the power of data to personalize the user experience and improve customer engagement. Machine learning techniques are an effective way to analyse large datasets of user behavior and generate accurate and relevant recommendations. In this project, we propose a machine learning-based movie recommendation system that uses content-based filtering techniques to generate personalized recommendations for users. Our system takes into account the user's viewing history, ratings, and preferences,

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P, Bhagya Sri, Sindhu Sri G, Jaya Sri K, Leela Poojitha V, and Sajida Sultana Sk. "Intelligent book recommendation system using ML techniques." ITM Web of Conferences 74 (2025): 03007. https://doi.org/10.1051/itmconf/20257403007.

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The current research focuses on a recommendation system based on Decision Tree, Naive Bayes, Ridge Classifier, and Random Forest, using a new hybrid method combining Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN). The Decision Tree model reaches a good trade-off for precision, recall, and F1 metrics, acting as a benchmark. On the other hand, the hybrid model greatly surpasses the remaining ones in such a way that precision is as high as 89.35%, recall is 59.01%, and F1 is up to 71.30%, thus reinforcing the notion that it finds user preferences for recommendations more effecti

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Ajith, Kumar, and Madhavan P. "Exploration of Collaboration Filtering Techniques for Product Recommendation." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 799–802. https://doi.org/10.35940/ijeat.C5348.029320.

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Today, recommendation system has been globally adopted as the most effective and reliable search engine for knowledge extraction in the field of education, economics and scientific research. Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences. In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.

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Ko, Hyeyoung, Suyeon Lee, Yoonseo Park, and Anna Choi. "A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields." Electronics 11, no. 1 (2022): 141. http://dx.doi.org/10.3390/electronics11010141.

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This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systemati

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Nazema, Syeda. "A Survey on Feature Recommendation Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 1662–68. http://dx.doi.org/10.17762/ijritcc2321-8169.1503167.

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Liu, Yixiang. "Deep Learning Based Music Recommendation Systems: A Review of Algorithms and Techniques." Applied and Computational Engineering 109, no. 1 (2024): 17–23. http://dx.doi.org/10.54254/2755-2721/109/20241353.

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Abstract. This paper provides a thorough examination of the utilisation of deep learning in music recommendation systems, which have transformed consumer discovery and engagement with music on streaming platforms. Scalability challenges and the cold-start problem are among the constraints that conventional recommendation methods, such as content-based filtering and collaborative filtering, encounter, which hinder their ability to deliver personalised recommendations that are effective. The processing of multi-modal and sequential data is significantly improved by deep learning methodologies, i

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Mini, T. V. "Recommender Systems: Enhancing Prediction Accuracy Through Hybrid Data Mining Techniques." International Journal of Information Technology Research Studies (IJITRS) 1, no. 1 (2025): 7–19. https://doi.org/10.5281/zenodo.15309672.

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This research explores the integration of multiple data mining approaches to improve recommendation accuracy in modern recommender systems. Despite significant advancements in recommendation algorithms, challenges persist in addressing the cold-start problem, data sparsity, and preference volatility. This study investigates how hybrid techniques combining collaborative filtering, content-based filtering, and knowledge-based approaches can overcome these limitations. Using a comprehensive dataset from an e-commerce platform with 2.3 million user-item interactions, we implemented a novel hybrid

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Ajay, Agarwal*1 &. Minakshi2. "EDUCATIONAL DATA SETS AND TECHNIQUES OF RECOMMENDER SYSTEMS: A SURVEY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 10 (2017): 434–43. https://doi.org/10.5281/zenodo.1036288.

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Due to growth of World Wide Web, enormous data are created. To get the information out of available data it is necessary to store these data in a particular format. These formatted data are called datasets. These datasets are important for extracting information in such a way so that decision can be taken to recommend the trend embedded in the datasets. In addition, they can be used to test and train many information processing applications. A general practice to use available datasets obtained from different application environments is to evaluate developed recommendation techniques. Such tec

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Prof., Vaishali V. Jikar Janvi Pandey Vrushabh Baraskar Hrushikesh Thorat Reshma Zade. "Book Recommendation System Using Machine Learning." International Journal of Advanced Innovative Technology in Engineering 9, no. 3 (2024): 267–70. https://doi.org/10.5281/zenodo.12736812.

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Recommendation System (RS) is software that suggests similar items to a purchaser based on his/her earlier purchases or preferences. RS examines huge data of objects and compiles a list of those objects which would fulfil the requirements of the buyer. Nowadays most ecommerce companies are using Recommendation systems to lure buyers to purchase more by offering items that the buyer is likely to prefer. Book Recommendation System is being used by Amazon, Barnes and Noble, Flipkart, Goodreads, etc. to recommend books the customer would be tempted to buy as they are matched with his/her choices.

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Das, Joydeep, Subhashis Majumder, and Kalyani Mali. "Clustering Techniques to Improve Scalability and Accuracy of Recommender Systems." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 04 (2021): 621–51. http://dx.doi.org/10.1142/s0218488521500276.

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Recommender systems have emerged as a class of essential tools in the success of modern e-commerce applications. These applications typically handle large datasets and often face challenges like data sparsity and scalability. Clustering techniques help to reduce the computational time needed for recommendation as well as handle the sparsity problem more efficiently. Traditional clustering based recommender systems create partitions (clusters) of the user-item rating matrix and execute the recommendation algorithm in the clusters separately in order to decrease the overall runtime of the system

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Waqar, Muhammad, and Mubbashir Ayub. "A personalized reinforcement learning recommendation algorithm using bi-clustering techniques." PLOS ONE 20, no. 2 (2025): e0315533. https://doi.org/10.1371/journal.pone.0315533.

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Recommender systems have become a core component of various online platforms, helping users get relevant information from the abundant digital data. Traditional RSs often generate static recommendations, which may not adapt well to changing user preferences. To address this problem, we propose a novel reinforcement learning (RL) recommendation algorithm that can give personalized recommendations by adapting to changing user preferences. However, a significant drawback of RL-based recommendation systems is that they are computationally expensive. Moreover, these systems often fail to extract lo

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Shargunam, S., and G. Rajakumar. "Filtering Techniques in Recommendation Systems: A Review." Asian Journal of Science and Applied Technology 10, no. 2 (2021): 22–25. http://dx.doi.org/10.51983/ajsat-2021.10.2.3059.

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Recommendation systems are not new to the world, they have rapidly become prevalent, appearing in almost every type of technology on a daily basis. As a result, recommendation systems were necessary to reduce the amount of time spent looking for the best and most essential items. Information filtering, user personalization, collaborative filtering, and hybrid filtering are just some of the ways used by recommendation systems in diversion, streaming, software, and other areas to present users and customers with customized content and products. The various filtering methods are compared and anal

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Bateja, Ritika, Sanjay Kumar Dubey, and Ashutosh Kumar Bhatt. "Diabetes Prediction and Recommendation Model Using Machine Learning Techniques and MapReduce." Indian Journal Of Science And Technology 17, no. 26 (2024): 2747–53. http://dx.doi.org/10.17485/ijst/v17i26.530.

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Objectives: To deliver patient centric healthcare for diabetic patients using a fast and efficient diabetic prediction and recommendation model which will not only help in early diagnoses of disease but also recommend appropriate medicine for controlling it at stage 1. Methods: The Support Vector Machine Classifier is further enhanced with Particle Swarm Optimization (PSO) and used for the prediction of diabetes. Collaborative Filtering is used for drug recommendation, which produces a suitable list of medications that correspond to the diagnoses of diabetes patients. Improved Density-Based Sp

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Navachaitanya, S. "E-Commerce product recommendation using machine learning techniques." i-manager's Journal on Information Technology 13, no. 4 (2024): 36. https://doi.org/10.26634/jit.13.4.21455.

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Machine learning is progressively being adopted by e-commerce platforms to enhance the shopping experience for consumers. By utilizing machine learning, large user datasets are analyzed to effectively forecast customer preferences, allowing for more relevant and personalized recommendations. Techniques such as collaborative filtering predict interests based on groups of similar users, while clustering, or segmentation, is employed for both users and items. This approach helps mitigate issues related to data sparsity and the cold-start challenge when it comes to generating valuable recommendati

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Nagraj, Shruthi, and Blessed Prince Palayyan. "Personalized E-commerce based recommendation systems using deep-learning techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 610. http://dx.doi.org/10.11591/ijai.v13.i1.pp610-618.

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As technology is surpassing each day, with the variation of personalized drifts relevant to the explicit behavior of users using the internet. Recommendation systems use predictive mechanisms like predicting a rating that a customer could give on a specific item. This establishes a ranked list of items according to the preferences each user makes concerning exhibiting personalized recommendations. The existing recommendation techniques are efficient in systematically creating recommendation techniques. This approach encounters many challenges such as determining the accuracy, scalability, and

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Nagaraj, Shruthi, and Palayyan Blessed Prince. "Personalized E-commerce based recommendation systems using deep-learning techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 610–18. https://doi.org/10.11591/ijai.v13.i1.pp610-618.

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As technology is surpassing each day, with the variation of personalized drifts relevant to the explicit behavior of users using the internet. Recommendation systems use predictive mechanisms like predicting a rating that a customer could give on a specific item. This establishes a ranked list of items according to the preferences each user makes concerning exhibiting personalized recommendations. The existing recommendation techniques are efficient in systematically creating recommendation techniques. This approach encounters many challenges such as determining the accuracy, scalability, and

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Zhou Zou, Sharin Hazlin Huspi, and Ahmad Najmi Amerhaider Nuar. "A Review on Job Recommendation System." Journal of Advanced Research in Applied Sciences and Engineering Technology 41, no. 2 (2024): 113–24. http://dx.doi.org/10.37934/araset.41.2.113124.

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With the rapid growth of artificial intelligence and machine learning technologies, the recommendation system aims to help users find items that match their preferences. In order to improve performance, many recommendation system techniques have been proposed. This paper presents a survey of some common recommendation techniques and related issues with advantages and disadvantages. At the same time, the different types of job recommendation systems are described in detail and compared with each other. The goal is to provide a comprehensive overview of the current state of job recommendation sy

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Kumar, Praveen, Mukesh Kumar Gupta, Channapragada Rama Seshagiri Rao, M. Bhavsingh, and M. Srilakshmi. "A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (2023): 184–92. http://dx.doi.org/10.17762/ijritcc.v11i3s.6180.

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Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recom

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Surati, Alpesh K. "A Survey of Recommendation System." RESEARCH REVIEW International Journal of Multidisciplinary 03, no. 05 (2018): 223–27. https://doi.org/10.5281/zenodo.1253472.

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Recommendation is a process which plays an important role in many applications . Main objective of this paper is to show various challenges regarding to the techniques that are being used for generating recommendations. Recommendations techniques can be classified in to three major categories: Collaborative Filtering, Content Based and Hybrid Recommendations. By giving the overview of these problems we can improve the quality of recommendations by inventing new approaches and methods, which can be used as a highway for research and practice in this area.

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Angira, Patel*1 &., and JyotindraDharwa2 Dr. "GRAPH-BASED RECOMMENDATION MODEL ENVISIONED FOR VARIOUS DOMAINS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 12 (2017): 38–45. https://doi.org/10.5281/zenodo.1115385.

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Recommender systems are envisioned and design to serve automatic recommendations for various services and products to active consumer. Such systems can find similar items and sort to generate top N suggestions as per users past transaction, location, knowledge, profiles, preferences or choices of otherpeople. This research illustrates potential use of graph-based model intended for recommendation system and designed for various domains. The ultramodern graph technology and state-of-the-art graph query tool is prime motivation behind this research work. The implementation has been carried out w

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Zou, Zhou, Sharin Hazlin Huspi, Ahmad Najmi Amerhaider Nuar, and Kebiao Zhu. "A Conceptual Framework of Career Move Recommendation System." Journal of Advanced Research Design 126, no. 1 (2025): 91–98. https://doi.org/10.37934/ard.126.1.9198.

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Nowadays, job recommendation systems are becoming more and more popular for job seekers to generate personalized job recommendations, but it is increasingly challenging as the techniques used are changing rapidly. Most of the existing job recommendation systems only consider the user’s interests, without consideration of the user’s skills, which can help them to make a career move. In this paper, the problem was addressed by applying the Design Science Research Methodology to propose an artefact. The proposed conceptual framework generates personalized job and skill recommendations for a caree

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Jajoo, Palika, and Dolly Mittal. "A Review on Techniques of Recommendation System." SKIT Research Journal 11, no. 2 (2021): 31. http://dx.doi.org/10.47904/ijskit.11.2.2021.31-36.

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Lu, Jinliang. "A Survey of Online Course Recommendation Techniques." Open Journal of Applied Sciences 12, no. 01 (2022): 134–54. http://dx.doi.org/10.4236/ojapps.2022.121010.

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Behl, Rachna, and Indu Kashyap. "Locus recommendation using probabilistic matrix factorization techniques." Ingeniería Solidaria 17, no. 1 (2021): 1–25. http://dx.doi.org/10.16925/2357-6014.2021.01.10.

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Introduction: The present paper is the outcome of the research “Locus Recommendation using Probabilistic Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20.  Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to the users.   Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF. This is because the technique is based on gamma distribution to the model user and item matrix. U

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Adomavicius, Gediminas, and YoungOk Kwon. "New Recommendation Techniques for Multicriteria Rating Systems." IEEE Intelligent Systems 22, no. 3 (2007): 48–55. http://dx.doi.org/10.1109/mis.2007.58.

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Abdullahi, Muhammad Umar, Morufu Olalere, Gilbert I. O. Aimufua, Kene Tochukwu Anyachebelu, and Bako Halilu Egga. "Crop Recommendation Predictive Analysis using Ensembling Techniques." Journal of Basics and Applied Sciences Research 2, no. 1 (2024): 162–76. http://dx.doi.org/10.33003/jobasr-2024-v2i1-43.

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Crop recommendation systems play a crucial role in modern agriculture by aiding farmers in making well-informed choices to optimize crop yield and resource utilization. Ensemble learning approaches can significantly improve the effectiveness of crop recommendation systems. To achieve this, multiple forecasts are combined from various models. In this paper, a complete Machine Learning Pipeline is used to evaluate the performance of ensemble learning models in crop recommendation tasks. A diverse dataset is used to select and train four ensemble learning methods, Bagging, Voting, Stacking, and O

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Bhopale, Prajyot P. "Music Recommendation System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1234–37. https://doi.org/10.22214/ijraset.2025.68469.

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Music recommendation systems function as personalized assistants that analyze listener preferences and suggest relevant songs or playlists. These systems utilize past user data to generate recommendations that align with individual tastes. However, users often struggle to identify the most suitable songs due to the vast availability of music content. Various techniques have been employed to enhance recommendation accuracy, including collaborative filtering, content-based filtering, and hybrid models. Initially, the system gathers substantial user data, such as listening history and ratings, to

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Alghamdi, Abdullah. "Leveraging Spectral Clustering and Long Short-Term Memory Techniques for Green Hotel Recommendations in Saudi Arabia." Sustainability 17, no. 5 (2025): 2328. https://doi.org/10.3390/su17052328.

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Online recommendation agents have demonstrated their value in various contexts by helping users navigate information overload, supporting decision-making, and influencing user behavior. There is a lack of studies focusing on recommendation systems for green hotels that utilize user-generated content from social networking and e-commerce platforms. While numerous studies have explored the use of real-world datasets for hotel recommendations, the development of recommendation systems specifically for green hotels remains underexplored, particularly in the context of Saudi Arabia. This study atte

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Sofikitis, Evangelos, and Christos Makris. "Development of recommendation systems using game theoretic techniques." Computer Science and Information Systems, no. 00 (2022): 18. http://dx.doi.org/10.2298/csis210925018s.

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In the present work, we inquire the use of game theoretic techniques for the development of recommender systems. Initially, the interaction of the two aspects of the systems, query reformulation and relevance estimation, is modelled as a cooperative game where the two players have a common utility, to supply optimal recommendations, which they try to maximize. Based on this modelling, three basic recommendation methods are developed, namely collaborative filtering, content based filtering and demographic filtering. The different methods are then combined to create hybrid systems. In the weight

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Astrit, Desku. "Methods and Techniques for Recommender Systems in Secure Software Engineering: A Literature Review." International Journal of Innovative Science and Research Technology 7, no. 3 (2022): 1430–36. https://doi.org/10.5281/zenodo.6496507.

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Recommender Systems are software tools that can assist developers with a wide range of activities, from reusing codes to suggest developers what to do during development of these systems. All recommender systems should exert one or three of future functionalities: Gathering Data and Creating Dataset, Static Analysis and Recommendation to user-by-user interface. In this paper, we have presented a literature review in the field of recommender systems. Papers are aggregating by their context in three main groups: Mechanism to Collect Data, Recommendation Engine to Analyze Data and Generate Recomm

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Wiyono, Slamet, and Rais Rais. "The Use of Clustering Methods in Memory-Based Collaborative Filtering for Ranking-Based Recommendation Systems." Buletin Ilmiah Sarjana Teknik Elektro 5, no. 4 (2024): 599–605. https://doi.org/10.12928/biste.v5i4.9435.

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This research explores the application of clustering techniques and frequency normalization in collaborative filtering to enhance the performance of ranking-based recommendation systems. Collaborative filtering is a popular approach in recommendation systems that relies on user-item interaction data. In ranking-based recommendation systems, the goal is to provide users with a personalized list of items, sorted by their predicted relevance. In this study, we propose a novel approach that combines clustering and frequency normalization techniques. Clustering, in the context of data analysis, is

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S, Dr Brinthakumari. "AgroGenius: Advanced Crop Recommendation System using Machine Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31627.

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The agricultural sector faces the pressing challenge of optimizing crop selection to ensure sustainable yield amidst dynamic environmental conditions and evolving market demands. To address this challenge, This paper presents a Crop Recommendation System (CRS) designed to assist farmers in optimizing crop selection. Utilizing machine learning, historical data, soil analysis, weather patterns, and market trends, CRS delivers personalized recommendations. Through user-friendly interfaces accessible via web or mobile platforms, farmers input site-specific information to receive real-time tailored

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Arifoğulları, Ömer, Günce Keziban Orman, and Gülfem Işıklar Alptekin. "Enhancing Hotel Recommendations through Feature- based Clustering." Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi 12, no. 1 (2025): 233–41. https://doi.org/10.35193/bseufbd.1513170.

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This paper addresses the challenge of sparse interaction data in recommendation systems for the hotel industry. Due to the infrequent nature of hotel stays (often once or a few times annually), customer-product interaction data is typically sparse, hindering the effectiveness of traditional collaborative filtering techniques. We propose a novel hybrid recommendation framework specifically designed for this scenario. Unlike conventional systems that rely solely on user preference similarity, our framework leverages hotel clustering based on binary attributes to segment the product space. User i

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N. Thangarasu, R. Rajalakshmi, G. Manivasagam, and V. Vijayalakshmi. "Performance of re-ranking techniques used for recommendation method to the user CF- Model." International Journal of Data Informatics and Intelligent Computing 1, no. 1 (2022): 30–38. http://dx.doi.org/10.59461/ijdiic.v1i1.9.

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The recent research work for addressed to the aims at a spectrum of item ranking techniques that would generate recommendations with far more aggregate variability across all users while retaining comparable levels of recommendation accuracy. Individual users and companies are increasingly relying on recommender systems to provide information on individual suggestions. The recommended technologies are becoming increasingly efficient because they are focusing on scalable sorting-based heuristics that make decisions based solely on "local" data (i.e., only on the candidate items of each user) ra

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N., Thangarasu, Rajalakshmi R., Manivasagam G., and Vijayalakshmi V. "Performance of re-ranking techniques used for recommendation method to the user CF- Model." International Journal of Data Informatics and Intelligent computing 1, no. 1 (2022): 30–38. https://doi.org/10.5281/zenodo.7108931.

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The recent research work for addressed to the aims at a spectrum of item ranking techniques that would generate recommendations with far more aggregate variability across all users while retaining comparable levels of recommendation accuracy. Individual users and companies are increasingly relying on recommender systems to provide information on individual suggestions. The recommended technologies are becoming increasingly efficient because they are focusing on scalable sorting-based heuristics that make decisions based solely on "local" data (i.e., only on the candidate items of eac

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Li, Jinxu. "Research on Music Neighborhood-Based Recommendation Algorithms." Applied and Computational Engineering 111, no. 1 (2024): 124–30. http://dx.doi.org/10.54254/2755-2721/111/2024ch0098.

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This paper presents a comprehensive review of recommendation algorithms in the music domain. Music recommendation systems play a crucial role in delivering personalized content to users by analyzing patterns and preferences. We analyze several prominent recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid models, each of which has been extensively applied to music recommendation tasks. The study also delves into the emerging techniques of deep learning and reinforcement learning in improving recommendation accuracy. A comparative analysis is provide

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S Nirmala Devi. "Non-Singular Convoluted Matrix Collaborative with Filtering and Sampling Techniques." Communications on Applied Nonlinear Analysis 32, no. 2 (2024): 533–49. http://dx.doi.org/10.52783/cana.v32.1803.

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Due to the comprehensive and accessible knowledge they provide, social media platforms are developed as prominent technologies. The community strategy remains as a repository of millions of individuals for numerous application, include evaluations concerning health, services preferences investigation, and numerous others. And use this information, social media network personalized recommendation algorithms allow the user to interactively choose their alternatives via inter networks. It makes reasonable because content - based recommendation model should indeed be adequately arranged to work ou

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Sharma, Garvit, Karthik Pragada, Poushali Deb Purkayastha, and Yukta Vajpayee. "Research Paper on Exploring the Landscape of Recommendation Systems: A Comparative Analysis of Techniques and Approaches." International Journal of Engineering and Computer Science 13, no. 06 (2024): 26196–218. http://dx.doi.org/10.18535/ijecs/v13i06.4827.

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The field of recommendation systems has witnessed a profound evolution since its inception with Grundy, the first computer-based librarian, in 1979. From its humble beginnings, recommendation systems have become integral to various facets of daily life, particularly in e-commerce, thanks to breakthroughs like Amazon’s Collaborative Filtering in the late 1990s. This led to widespread adoption across diverse sectors, prompting significant research interest and investment, exemplified by Netflix’s renowned recommendation system contest in 2006. Today, recommendation systems employ various techniq

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Aldayel, Mashael, Abeer Al-Nafjan, Waleed M. Al-Nuwaiser, Ghadeer Alrehaili, and Ghadi Alyahya. "Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations." Electronics 12, no. 19 (2023): 4047. http://dx.doi.org/10.3390/electronics12194047.

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The success of touristic businesses, attractions, and destinations heavily relies on travel agents’ recommendations, which significantly impact client satisfaction. However, the underlying recommendation process employed by travel agents remains poorly understood. This study presents a conceptual model of the recommendation process and empirically investigates the influence of tourism categories on agents’ destination recommendations. By employing collaborative filtering-based recommendation systems and comparing various algorithms, including matrix factorization and deep learning models, such

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Mahmood, Wisam Alnadem, LaythKamil Almajmaie, Ahmed Raad Raheem, and Saad Albawi. "A hybrid approach towards movie recommendation system with collaborative filtering and association rule mining." Acta Scientiarum. Technology 44 (March 11, 2022): e58925. http://dx.doi.org/10.4025/actascitechnol.v44i1.58925.

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There is a huge information stockpile available on the internet. But the available information still throws a stiff challenge to users while selecting the needed information. Such an issue can be solved by applying information filtering for locating the required information through a Recommender System. While using a RS, the users find it easy to curate and collect relevant information out of massive databanks. Though various types of RS are currently available, yet the RS developed by Collaborative Filtering techniques has proven to be the most suitable for many problems. Among the various Re

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Huang, Xiao, Pengjie Ren, Zhaochun Ren, et al. "Report on the international workshop on natural language processing for recommendations (NLP4REC 2020) workshop held at WSDM 2020." ACM SIGIR Forum 54, no. 1 (2020): 1–5. http://dx.doi.org/10.1145/3451964.3451970.

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This paper summarizes the outcomes of the International Workshop on Natural Language Processing for Recommendations (NLP4REC 2020), held in Houston, USA, on February 7, 2020, during WSDM 2020. The purpose of this workshop was to explore the potential research topics and industrial applications in leveraging natural language processing techniques to tackle the challenges in constructing more intelligent recommender systems. Specific topics included, but were not limited to knowledge-aware recommendation, explainable recommendation, conversational recommendation, and sequential recommendation.

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O'DONOVAN, JOHN, and BARRY SMYTH. "MINING TRUST VALUES FROM RECOMMENDATION ERRORS." International Journal on Artificial Intelligence Tools 15, no. 06 (2006): 945–62. http://dx.doi.org/10.1142/s0218213006003053.

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Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users' tastes and opinions. Our work focusses on these similarities and the fact that current techniques for defining which users contribute to recommendation are in need of improvement. In this paper we propose the use of trustworthiness as an improvement

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