CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)

• URL: http://arxiv.org/abs/2406.10235v1
• Date: Fri, 31 May 2024 14:50:53 GMT
• Title: CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)
• Authors: Sajida Mhammedi, Hakim El Massari, Noreddine Gherabi, Amnai Mohamed,
• Abstract summary: This work is to address the recommender system's data sparsity and accuracy problems. The implemented approach efficiently reduces the sparsity of CF suggestions, improves their accuracy, and gives more relevant items as recommendations.
• Score: 0.0
• License:
• Abstract: Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. However, due to data overloading, recommender systems struggle to handle large volumes of data reliably and accurately before offering suggestions. The main purpose of this work is to address the recommender system's data sparsity and accuracy problems by using the matrix factorization algorithm of collaborative filtering based on the dimensional reduction method and, more precisely, the Nonnegative Matrix Factorization (NMF) combined with ontology. We tested the method and compared the results to other classic methods. The findings showed that the implemented approach efficiently reduces the sparsity of CF suggestions, improves their accuracy, and gives more relevant items as recommendations.

Related papers

• Preference Diffusion for Recommendation [50.8692409346126]
  We propose PreferDiff, a tailored optimization objective for DM-based recommenders. PreferDiff transforms BPR into a log-likelihood ranking objective to better capture user preferences. It is the first personalized ranking loss designed specifically for DM-based recommenders.
  arXiv  Detail & Related papers  (2024-10-17T01:02:04Z)
• Data Imputation using Large Language Model to Accelerate Recommendation System [3.853804391135035]
  We propose a novel approach that fine-tune Large Language Model (LLM) and use it impute missing data for recommendation systems. LLM which is trained on vast amounts of text, is able to understand complex relationship among data and intelligently fill in missing information. This enriched data is then used by the recommendation system to generate more accurate and personalized suggestions.
  arXiv  Detail & Related papers  (2024-07-14T04:53:36Z)
• Transforming Movie Recommendations with Advanced Machine Learning: A Study of NMF, SVD,and K-Means Clustering [0.0]
  This study develops a robust movie recommendation system using various machine learning techniques. The primary objective is to enhance user experience by providing personalized movie recommendations.
  arXiv  Detail & Related papers  (2024-07-12T01:26:33Z)
• Recommendation Unlearning via Matrix Correction [17.457533987238975]
  We propose an Interaction and Mapping Matrices Correction (IMCorrect) method for recommendation unlearning. We show that IMCorrect is superior in completeness, utility, and efficiency, and is applicable in many recommendation unlearning scenarios.
  arXiv  Detail & Related papers  (2023-07-29T11:36:38Z)
• Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach [56.12815715932561]
  We propose a new broad recommender system called Broad Collaborative Filtering (BroadCF) Instead of Deep Neural Networks (DNNs), Broad Learning System (BLS) is used as a mapping function to learn the complex nonlinear relationships between users and items. Extensive experiments conducted on seven benchmark datasets have confirmed the effectiveness of the proposed BroadCF algorithm.
  arXiv  Detail & Related papers  (2022-04-20T01:25:08Z)
• GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation [0.0]
  We propose a recommender system method using a graph-based model associated with the similarity of users' ratings. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.
  arXiv  Detail & Related papers  (2021-11-06T10:47:45Z)
• Reenvisioning Collaborative Filtering vs Matrix Factorization [65.74881520196762]
  Collaborative filtering models based on matrix factorization and learned similarities using Artificial Neural Networks (ANNs) have gained significant attention in recent years. Announcement of ANNs within the recommendation ecosystem has been recently questioned, raising several comparisons in terms of efficiency and effectiveness. We show the potential these techniques may have on beyond-accuracy evaluation while analyzing effect on complementary evaluation dimensions.
  arXiv  Detail & Related papers  (2021-07-28T16:29:38Z)
• Modurec: Recommender Systems with Feature and Time Modulation [50.51144496609274]
  We propose Modurec: an autoencoder-based method that combines all available information using the feature-wise modulation mechanism. We show on Movielens datasets that these modifications produce state-of-the-art results in most evaluated settings.
  arXiv  Detail & Related papers  (2020-10-13T09:18:33Z)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)
• Providing reliability in Recommender Systems through Bernoulli Matrix Factorization [63.732639864601914]
  This paper proposes Bernoulli Matrix Factorization (BeMF) to provide both prediction values and reliability values. BeMF acts on model-based collaborative filtering rather than on memory-based filtering. The more reliable a prediction is, the less liable it is to be wrong.
  arXiv  Detail & Related papers  (2020-06-05T14:24:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.