scholarly journals Temporal Matrix Factorization for Tracking Concept Drift in Individual User Preferences

2018 ◽  
Vol 5 (1) ◽  
pp. 156-168 ◽  
Author(s):  
Yung-Yin Lo ◽  
Wanjiun Liao ◽  
Cheng-Shang Chang ◽  
Ying-Chin Lee
Keyword(s):  
Matrix Factorization ◽  
Concept Drift ◽  
User Preferences ◽  
Individual User

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

Content-Enhanced Matrix Factorization for Recommender Systems

2013 ◽  
Vol 475-476 ◽  
pp. 1084-1089
Author(s):  
Hui Yuan Chang ◽  
Ding Xia Li ◽  
Qi Dong Liu ◽  
Rong Jing Hu ◽  
Rui Sheng Zhang
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Factor Models ◽  
User Preferences ◽  
Latent Factor ◽  
Successful Approach ◽  
Latent Factor Models ◽  
User Ratings ◽  
Important Measurement ◽  
Potential Customers

Recommender systems are widely employed in many fields to recommend products, services and information to potential customers. As the most successful approach to recommender systems, collaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. It can be divided into two main braches - the neighbourhood approach (NB) and latent factor models. Some of the most successful realizations of latent factor models are based on matrix factorization (MF). Accuracy is one of the most important measurement criteria for recommender systems. In this paper, to improve accuracy, we propose an improved MF model. In this model, we not only consider the latent factors describing the user and item, but also incorporate content information directly into MF.Experiments are performed on the Movielens dataset to compare the present approach with the other method. The experiment results indicate that the proposed approach can remarkably improve the recommendation quality.

scholarly journals A General Cooperative Optimization Approach for Distributing Service Points in Mobility Applications

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 232
Author(s):  
Thomas Jatschka ◽  
Günther R. Raidl ◽  
Tobias Rodemann
Keyword(s):  
Objective Function ◽  
Matrix Factorization ◽  
Large Scale ◽  
User Interaction ◽  
User Preferences ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Mixed Integer Linear Program ◽  
User Interactions ◽  
The Matrix

This article presents a cooperative optimization approach (COA) for distributing service points for mobility applications, which generalizes and refines a previously proposed method. COA is an iterative framework for optimizing service point locations, combining an optimization component with user interaction on a large scale and a machine learning component that learns user needs and provides the objective function for the optimization. The previously proposed COA was designed for mobility applications in which single service points are sufficient for satisfying individual user demand. This framework is generalized here for applications in which the satisfaction of demand relies on the existence of two or more suitably located service stations, such as in the case of bike/car sharing systems. A new matrix factorization model is used as surrogate objective function for the optimization, allowing us to learn and exploit similar preferences among users w.r.t. service point locations. Based on this surrogate objective function, a mixed integer linear program is solved to generate an optimized solution to the problem w.r.t. the currently known user information. User interaction, refinement of the matrix factorization, and optimization are iterated. An experimental evaluation analyzes the performance of COA with special consideration of the number of user interactions required to find near optimal solutions. The algorithm is tested on artificial instances, as well as instances derived from real-world taxi data from Manhattan. Results show that the approach can effectively solve instances with hundreds of potential service point locations and thousands of users, while keeping the user interactions reasonably low. A bound on the number of user interactions required to obtain full knowledge of user preferences is derived, and results show that with 50% of performed user interactions the solutions generated by COA feature optimality gaps of only 1.45% on average.

scholarly journals Building Recommendation Systems Using the Algorithms KNN and SVD

2021 ◽  
Vol 9 (1) ◽  
pp. 71
Author(s):  
Mohammed Erritali ◽  
Badr Hssina ◽  
Abdelkader Grota
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Singular Value ◽  
User Preferences ◽  
Factorization Algorithm ◽  
The Matrix ◽  
Filtering Approach

<p>Recommendation systems are used successfully to provide items (example:<br />movies, music, books, news, images) tailored to user preferences.<br />Among the approaches proposed, we use the collaborative filtering approach<br />of finding the information that satisfies the user by using the<br />reviews of other users. These ratings are stored in matrices that their<br />sizes increase exponentially to predict whether an item is interesting<br />or not. The problem is that these systems overlook that an assessment<br />may have been influenced by other factors which we call the cold start<br />factor. Our objective is to apply a hybrid approach of recommendation<br />systems to improve the quality of the recommendation. The advantage<br />of this approach is the fact that it does not require a new algorithm<br />for calculating the predictions. We we are going to apply the two Kclosest<br />neighbor algorithms and the matrix factorization algorithm of<br />collaborative filtering which are based on the method of (singular value<br />decomposition).</p>

Matrix factorization completed multicontext data for tensor-enhanced recommendation

2021 ◽  
pp. 1-12
Author(s):  
Shangju Deng ◽  
Jiwei Qin
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
Matrix Factorization ◽  
Cold Start ◽  
User Preferences ◽  
High Dimensional ◽  
Satisfactory Performance ◽  
Interactive Data ◽  
Real World Datasets ◽  
Item Data

Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse.

scholarly journals Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

2018 ◽  
Vol 11 (2) ◽  
pp. 1 ◽  
Author(s):  
Mohamed Hussein Abdi ◽  
George Onyango Okeyo ◽  
Ronald Waweru Mwangi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Large Scale ◽  
User Preferences ◽  
Online Information ◽  
Context Aware ◽  
Preference Rating ◽  
Accessible Information ◽  
The Impact

Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems. 

scholarly journals Improving Top-NRecommendation Performance Using Missing Data

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xiangyu Zhao ◽  
Zhendong Niu ◽  
Kaiyi Wang ◽  
Ke Niu ◽  
Zhongqiang Liu
Keyword(s):  
Missing Data ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
State Of The Art ◽  
User Preferences ◽  
Missing Not At Random ◽  
Main Challenge ◽  
Recommendation Algorithms ◽  
Random Part ◽  
Problem Data

Recommender systems become increasingly significant in solving the information explosion problem. Data sparse is a main challenge in this area. Massive unrated items constitute missing data with only a few observed ratings. Most studies consider missing data as unknown information and only use observed data to learn models and generate recommendations. However, data are missing not at random. Part of missing data is due to the fact that users choose not to rate them. This part of missing data is negative examples of user preferences. Utilizing this information is expected to leverage the performance of recommendation algorithms. Unfortunately, negative examples are mixed with unlabeled positive examples in missing data, and they are hard to be distinguished. In this paper, we propose three schemes to utilize the negative examples in missing data. The schemes are then adapted with SVD++, which is a state-of-the-art matrix factorization recommendation approach, to generate recommendations. Experimental results on two real datasets show that our proposed approaches gain better top-Nperformance than the baseline ones on both accuracy and diversity.

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