A fusion of aspect and contextual information for rating prediction in recommender system using a latent factor model

2021 ◽  
Vol 16 (1) ◽  
pp. 30
Author(s):  
Jitali Patel ◽  
Hitesh Chhinkaniwala
Keyword(s):  
Recommender System ◽  
Factor Model ◽  
Contextual Information ◽  
Latent Factor ◽  
Latent Factor Model ◽  
Rating Prediction

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 Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness

2017 ◽  
Author(s):  
Kaisong Song ◽  
Wei Gao ◽  
Shi Feng ◽  
Daling Wang ◽  
Kam-Fai Wong ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Factor Model ◽  
Cold Start ◽  
User Preferences ◽  
Supplementary Information ◽  
Latent Factor ◽  
Latent Factor Model ◽  
Rating Prediction ◽  
The Difference ◽  
Product Interaction

Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of reviews, user preferences and product characteristics by jointly optimizing two components, a user-specific LFM and a product-specific LFM, each of which decomposes text into a specific low-dimension representation. Furthermore, we address the cold-start issue by developing a novel Pairwise Rating Comparison strategy (PRC), which utilizes the difference between ratings on common user/product as supplementary information to calibrate parameter estimation. Experiments conducted on IMDB and Yelp datasets validate the advantage of our approach over state-of-the-art baseline methods.

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
Sign in / Sign up

Export Citation Format

Share Document