Latent Factor Model Applied to Recommender System: Realization, Steps and Algorithm

2017 ◽  
pp. 606-618 ◽  
Author(s):  
Maryam Jallouli ◽  
Sonia Lajmi ◽  
Ikram Amous
Keyword(s):  
Recommender System ◽  
Factor Model ◽  
Latent Factor ◽  
System Realization ◽  
Latent Factor Model

Aspect-based latent factor model by integrating ratings and reviews for recommender system

2016 ◽  
Vol 110 ◽  
pp. 233-243 ◽  
Author(s):  
Lin Qiu ◽  
Sheng Gao ◽  
Wenlong Cheng ◽  
Jun Guo
Keyword(s):  
Recommender System ◽  
Factor Model ◽  
Latent Factor ◽  
Latent Factor Model

scholarly journals Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Hanafi ◽  
Burhanuddin Mohd Aboobaider
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Factor Model ◽  
Sparse Data ◽  
Product Review ◽  
Latent Factor ◽  
Latent Factor Model ◽  
Future Work

Recommender systems are essential engines to deliver product recommendations for e-commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers’ activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF-IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF-IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e-commerce recommender systems with sparse data issues.

scholarly journals Cross-Domain Recommendation via Cluster-Level Latent Factor Model

2013 ◽  
pp. 161-176 ◽  
Author(s):  
Sheng Gao ◽  
Hao Luo ◽  
Da Chen ◽  
Shantao Li ◽  
Patrick Gallinari ◽  
...  
Keyword(s):  
Factor Model ◽  
Latent Factor ◽  
Cross Domain ◽  
Latent Factor Model ◽  
Cluster Level
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