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 Survey of Latent Factor Models for Recommender Systems and Personalization

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Matrix Completion ◽  
Factor Models ◽  
Singular Value ◽  
Bayesian Personalized Ranking ◽  
Latent Factor Models ◽  
Value Decomposition ◽  
Personalized Ranking ◽  
Non Negative Matrix Factorization

<p>Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.</p>

scholarly journals A Survey of Latent Factor Models for Recommender Systems and Personalization

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Matrix Completion ◽  
Factor Models ◽  
Singular Value ◽  
Bayesian Personalized Ranking ◽  
Latent Factor Models ◽  
Value Decomposition ◽  
Personalized Ranking ◽  
Non Negative Matrix Factorization

<p>Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.</p>

scholarly journals Unsupervised Deep Hashing via Binary Latent Factor Models for Large-scale Cross-modal Retrieval

2018 ◽  
Author(s):  
Gengshen Wu ◽  
Zijia Lin ◽  
Jungong Han ◽  
Li Liu ◽  
Guiguang Ding ◽  
...  
Keyword(s):  
Deep Learning ◽  
Objective Function ◽  
Matrix Factorization ◽  
Large Scale ◽  
Feature Learning ◽  
Factor Models ◽  
Great Success ◽  
Latent Factor ◽  
Unsupervised Deep Learning ◽  
Latent Factor Models

Despite its great success, matrix factorization based cross-modality hashing suffers from two problems: 1) there is no engagement between feature learning and binarization; and 2) most existing methods impose the relaxation strategy by discarding the discrete constraints when learning the hash function, which usually yields suboptimal solutions. In this paper, we propose a novel multimodal hashing framework, referred as Unsupervised Deep Cross-Modal Hashing (UDCMH), for multimodal data search in a self-taught manner via integrating deep learning and matrix factorization with binary latent factor models. On one hand, our unsupervised deep learning framework enables the feature learning to be jointly optimized with the binarization. On the other hand, the hashing system based on the binary latent factor models can generate unified binary codes by solving a discrete-constrained objective function directly with no need for a relaxation step. Moreover, novel Laplacian constraints are incorporated into the objective function, which allow to preserve not only the nearest neighbors that are commonly considered in the literature but also the farthest neighbors of data, even if the semantic labels are not available. Extensive experiments on multiple datasets highlight the superiority of the proposed framework over several state-of-the-art baselines.

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