CUPCF: Combining Users Preferences in Collaborative Filtering for Better Recommendation

2019-08-13 · Mostafa Khalaji, Nilufar Mohammadnejad

How to make the best decision between the opinions and tastes of your friends and acquaintances? Therefore, recommender systems are used to solve such issues. The common algorithms use a similarity measure to predict active users' tastes over a particular item. According to the cold start and data sparsity problems, these systems cannot predict and suggest particular items to users. In this paper, we introduce a new recommender system is able to find user preferences and based on it, provides the recommendations. Our proposed system called CUPCF is a combination of two similarity measures in collaborative filtering to solve the data sparsity problem and poor prediction (high prediction error rate) problems for better recommendation. The experimental results based on MovieLens dataset show that, combined with the preferences of the user's nearest neighbor, the proposed system error rate compared to a number of state-of-the-art recommendation methods improved. Furthermore, the results indicate the efficiency of CUPCF. The maximum improved error rate of the system is 15.5% and the maximum values of Accuracy, Precision and Recall of CUPCF are 0.91402, 0.91436 and 0.9974 respectively.

📄 PDF Abstract BibTeX arXiv:1908.05609

Code (0)

등록된 구현이 없습니다.

Tasks

Collaborative Filtering Recommendation Systems

Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems

2021-06-08 · Yinchong Yang, Florian Buettner

Recommender systems are often designed based on a collaborative filtering approach, where user preferences are predicted by modelling interactions between users and items. Many common approaches to solve the collaborativ…

Collaborative Filtering Gaussian Processes Recommendation Systems Representation Learning

Enhanced Recommendation Combining Collaborative Filtering and Large Language Models

2024-12-25 · Xueting Lin, Zhan Cheng, Longfei Yun, Qingyi Lu 외

With the advent of the information explosion era, the importance of recommendation systems in various applications is increasingly significant. Traditional collaborative filtering algorithms are widely used due to their …

Collaborative Filtering Diversity Natural Language Understanding Recommendation Systems

Collaborative Metric Learning

2017-04-01 · WWW 2017 4 · Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin 외

Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work we study the connection between metric learning and collaborative filtering. We propose Collaborative …

Collaborative Filtering Metric Learning Recommendation Systems

Consistent Collaborative Filtering via Tensor Decomposition

2022-01-28 · Shiwen Zhao, Charles Crissman, Guillermo R Sapiro

Collaborative filtering is the de facto standard for analyzing users' activities and building recommendation systems for items. In this work we develop Sliced Anti-symmetric Decomposition (SAD), a new model for collabora…

Collaborative Filtering Recommendation Systems Tensor Decomposition

Collaborative Filtering for Predicting User Preferences for Organizing Objects

2015-12-20 · Abdo Nichola, Stachniss Cyrill, Spinello Luciano, Burgard Wolfram

As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, different users typically have their own pre…

Collaborative Filtering Common Sense Reasoning