Deep Variational Matrix Factorization with Knowledge Embedding for Recommendation System

2020 ◽  
pp. 1-1
Author(s):  
Xiaoxuan Shen ◽  
Baolin Yi ◽  
Hai Liu ◽  
Wei Zhang ◽  
Zhaoli Zhang ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Recommendation System ◽  
Variational Matrix

scholarly journals AANMF: Attribute-Aware Attentional Neural Matrix Factorization

2019 ◽  
Vol 48 (4) ◽  
pp. 682-693
Author(s):  
Bo Zheng ◽  
Jinsong Hu
Keyword(s):  
Matrix Factorization ◽  
Recommendation System ◽  
Auxiliary Information ◽  
Inner Product ◽  
Data Sets ◽  
It Projects ◽  
Real World Data ◽  
Latent Space ◽  
Almost All ◽  
Novel Model

Matrix Factorization (MF) is one of the most intuitive and effective methods in the Recommendation System domain. It projects sparse (user, item) interactions into dense feature products which endues strong generality to the MF model. To leverage this interaction, recent works use auxiliary information of users and items. Despite effectiveness, irrationality still exists among these methods, since almost all of them simply add the feature of auxiliary information in dense latent space to the feature of the user or item. In this work, we propose a novel model named AANMF, short for Attribute-aware Attentional Neural Matrix Factorization. AANMF combines two main parts, namely, neural-network-based factorization architecture for modeling inner product and attention-mechanism-based attribute processing cell for attribute handling. Extensive experiments on two real-world data sets demonstrate the robust and stronger performance of our model. Notably, we show that our model can deal with the attributes of user or item more reasonably. Our implementation of AANMF is publicly available at https://github.com/Holy-Shine/AANMF.

scholarly journals Recommendation Based on Trust Diffusion Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jinfeng Yuan ◽  
Li Li
Keyword(s):  
Matrix Factorization ◽  
High Performance ◽  
Recommendation System ◽  
Online Information ◽  
Trust Network ◽  
Data Sparsity ◽  
Pmf Model ◽  
Probabilistic Matrix Factorization ◽  
Sparsity Problem ◽  
Relationship Of

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, andFMeasure.

scholarly journals Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Huazhen Liu ◽  
Wei Wang ◽  
Yihan Zhang ◽  
Renqian Gu ◽  
Yaqi Hao
Keyword(s):  
Neural Network ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Heterogeneous Data ◽  
User Preference ◽  
Personalized Recommendation ◽  
Implicit Feedback ◽  
Network Training ◽  
Feedback Data ◽  
Explicit Feedback

Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.

scholarly journals Book Recommendation System using Matrix Factorization

2021 ◽  
Vol 9 (VI) ◽  
pp. 4578-4582
Author(s):  
K. Venkata Ruchitha
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Previous History ◽  
Speed Up ◽  
Area Unit ◽  
Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

scholarly journals Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1930
Author(s):  
Alpamis Kutlimuratov ◽  
Akmalbek Abdusalomov ◽  
Taeg Keun Whangbo
Keyword(s):  
Matrix Factorization ◽  
Recommendation System ◽  
Hierarchical Structures ◽  
The Matrix ◽  
Item Characteristics ◽  
Deep Relationship ◽  
Hierarchical Features ◽  
Novel Model ◽  
Prediction Efficiency ◽  
Non Negative Matrix Factorization

Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user’s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process.

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