A Cluster Based Collaborative Filtering Method for Improving the Performance of Recommender Systems in E-Commerce

2018 ◽  
pp. 990-1001
Author(s):  
Bahman Sassani ◽  
Alaa Alahmadi ◽  
Hamid Sharifzadeh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Filtering Method

scholarly journals Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

2021 ◽  
Vol 11 (24) ◽  
pp. 11890
Author(s):  
Silvana Vanesa Aciar ◽  
Ramón Fabregat ◽  
Teodor Jové ◽  
Gabriela Aciar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
User Experiences ◽  
Relevant Feedback ◽  
Filtering Method

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.

scholarly journals Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems

2020 ◽  
Vol 10 (14) ◽  
pp. 4926 ◽  
Author(s):  
Raúl Lara-Cabrera ◽  
Ángel González-Prieto ◽  
Fernando Ortega
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Sources Of Information ◽  
Factorization Approach ◽  
Filtering Method ◽  
Layered Architecture ◽  
Fundamental Part ◽  
Many Sources

Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users with accurate predictions and novel recommendations of items. Here we propose, DeepMF, a novel collaborative filtering method that combines the Deep Learning paradigm with Matrix Factorization (MF) to improve the quality of both predictions and recommendations made to the user. Specifically, DeepMF performs successive refinements of a MF model with a layered architecture that uses the acquired knowledge in a layer as input for subsequent layers. Experimental results showed that the quality of both the predictions and recommendations of DeepMF overcome the baselines.

Biclustering neighborhood-based collaborative filtering method for top-n recommender systems

2014 ◽  
Vol 44 (2) ◽  
pp. 475-491 ◽  
Author(s):  
Faris Alqadah ◽  
Chandan K. Reddy ◽  
Junling Hu ◽  
Hatim F. Alqadah
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Filtering Method

Combining Item Rating Similarity and Item Classification Similarity for Better Recommendation Quality

2012 ◽  
Vol 461 ◽  
pp. 289-292
Author(s):  
Kai Zhou
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Fundamental Problem ◽  
The Other ◽  
Recommendation Algorithm ◽  
Data Sparsity ◽  
Filtering Method ◽  
Product Recommendation ◽  
Sparsity Problem

Recommender systems are becoming increasingly popular, and collaborative filtering method is one of the most important technologies in recommender systems. The ability of recommender systems to make correct predictions is fundamentally determined by the quality and fittingness of the collaborative filtering that implements them. It is currently mainly used for business purposes such as product recommendation. Collaborative filtering has two types. One is user based collaborative filtering using the similarity between users to predict and the other is item based collaborative filtering using the similarity between items. Although both of them are successfully applied in wide regions, they suffer from a fundamental problem of data sparsity. This paper gives a personalized collaborative filtering recommendation algorithm combining the item rating similarity and the item classification similarity. This method can alleviate the data sparsity problem in the recommender systems

A fusion collaborative filtering method for sparse data in recommender systems

2020 ◽  
Vol 521 ◽  
pp. 365-379 ◽  
Author(s):  
Chenjiao Feng ◽  
Jiye Liang ◽  
Peng Song ◽  
Zhiqiang Wang
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Sparse Data ◽  
Filtering Method

Context-Aware Approach for Restaurant Recommender Systems

2019 ◽  
pp. 324-341 ◽  
Author(s):  
Haoxian Feng ◽  
Thomas Tran
Keyword(s):  
Root Mean Square Error ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Recommendation Algorithm ◽  
Filtering Method ◽  
User Behaviors ◽  
Svd Method

This chapter addresses the issue of how to effectively use users' historical data in restaurant recommender systems, as opposed to systems, such as FindMe, that only rely on online operations. Towards that end, the authors propose a bias-based SVD method as the underlying recommendation algorithm and test it against the traditional item-based collaborative filtering method on the Entrée restaurant dataset. The results are promising as the obtained root-mean-square-error (RMSE) values reach 0.58 for the SVD and 0.62 for the item-based system. Researchers can extend the transformation from user behaviors to ratings in more application domains other than the restaurant one.

Context-Aware Approach for Restaurant Recommender Systems

2018 ◽  
pp. 1757-1771
Author(s):  
Haoxian Feng ◽  
Thomas Tran
Keyword(s):  
Root Mean Square Error ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Recommendation Algorithm ◽  
Filtering Method ◽  
User Behaviors ◽  
Svd Method

This paper addresses the issue of how to effectively use users' historical data in restaurant recommender systems, as opposed to systems, such as FindMe, that only rely on online operations. Towards that end, the authors propose a bias-based SVD method as the underlying recommendation algorithm and test it against the traditional item-based collaborative filtering method on the Entrée restaurant dataset. The results are promising as the obtained Root-Mean-Square-Error (RMSE) values reach 0.58 for the SVD and 0.62 for the item-based system. Researchers can extend the transformation from user behaviors to ratings in more application domains other than the restaurant one.

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