Fuzzy-genetic approach to context-aware recommender systems based on the hybridization of collaborative filtering and reclusive method techniques

2019 ◽  
Vol 32 (2) ◽  
pp. 125-141 ◽  
Author(s):  
Sonal Linda ◽  
Sonajharia Minz ◽  
K.K. Bharadwaj
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Context Aware ◽  
Genetic Approach ◽  
Fuzzy Genetic

scholarly journals Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

2018 ◽  
Vol 11 (2) ◽  
pp. 1 ◽  
Author(s):  
Mohamed Hussein Abdi ◽  
George Onyango Okeyo ◽  
Ronald Waweru Mwangi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Large Scale ◽  
User Preferences ◽  
Online Information ◽  
Context Aware ◽  
Preference Rating ◽  
Accessible Information ◽  
The Impact

Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems. 

Improving Context Aware Recommendation Performance by Using Social Networks

2015 ◽  
Vol 8 (3) ◽  
pp. 73-87
Author(s):  
Golshan Assadat Afzali Boroujeni ◽  
Seyed Alireza Hashemi Golpayegani
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
High Volume ◽  
Context Aware ◽  
Mobile Data ◽  
The People ◽  
Proposed Model ◽  
The Social ◽  
Current Context

Ecommerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. In collaborative filtering - as the most popular method in recommender systems - an implicit network is formed among all the people. In any network, there are some individuals who have some inspirational power over the others leading them to influence their decisions and behaviours. But it seems that these methods do not support context awareness in mobile commerce environments. Furthermore, they lack high accuracy and also require high volume of computations due to not distinguish between neighbours as a friend or a stranger. This paper proposes a new model for recommender systems which are based on mobile data. This model uses these data to extract current users' context and also to identify individuals with the highest influence. Then, the system uses the information of these identified impressive users in the current context existed in the social networks for making recommendations. Beside of achieving higher accuracy, the proposed model has resolved cold start problem in collaborative filtering systems.

Improving Context Aware Recommendation Performance by Using Social Networks

2014 ◽  
Vol 7 (3) ◽  
pp. 1-14
Author(s):  
Golshan Assadat Afzali Boroujeni ◽  
Seyed Alireza Hashemi Golpayegani
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
High Volume ◽  
Context Aware ◽  
Mobile Data ◽  
The People ◽  
Proposed Model ◽  
The Social ◽  
Current Context

Ecommerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. In collaborative filtering—as the most popular method in recommender systems—an implicit network is formed among all the people. In any network, there are some individuals who have some inspirational power over the others leading them to influence their decisions and behaviours. But it seems that these methods do not support context awareness in mobile commerce environments. Furthermore, they lack high accuracy and also require high volume of computations due to not distinguish between neighbours as a friend or a stranger. This paper proposes a new model for recommender systems which are based on mobile data. This model uses these data to extract current users' context and also to identify individuals with the highest influence. Then, the system uses the information of these identified impressive users in the current context existed in the social networks for making recommendations. Beside of achieving higher accuracy, the proposed model has resolved cold start problem in collaborative filtering systems.

Fuzzy-genetic approach to recommender systems based on a novel hybrid user model

2008 ◽  
Vol 35 (3) ◽  
pp. 1386-1399 ◽  
Author(s):  
Mohammad Yahya H. Al-Shamri ◽  
Kamal K. Bharadwaj
Keyword(s):  
Recommender Systems ◽  
User Model ◽  
Genetic Approach ◽  
Fuzzy Genetic

Context-Aware Recommender Systems

2012 ◽  
Vol 23 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Li-Cai WANG ◽  
Xiang-Wu MENG ◽  
Yu-Jie ZHANG
Keyword(s):  
Recommender Systems ◽  
Context Aware

Production-System Optimization of Gas Fields Using Hybrid Fuzzy/Genetic Approach

SPE Journal ◽  
2010 ◽  
Vol 15 (02) ◽  
pp. 417-425 ◽  
Author(s):  
Hui-June Park ◽  
Jong-Se Lim ◽  
Jeongyong Roh ◽  
Joo M. Kang ◽  
Bae-Hyun Min
Keyword(s):  
Production System ◽  
System Optimization ◽  
Genetic Approach ◽  
Gas Fields ◽  
Fuzzy Genetic

scholarly journals Context-aware recommender systems and cultural heritage: a survey

2021 ◽  
Author(s):  
Mario Casillo ◽  
Francesco Colace ◽  
Dajana Conte ◽  
Marco Lombardi ◽  
Domenico Santaniello ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Recommender Systems ◽  
Context Awareness ◽  
Technological Development ◽  
Context Aware ◽  
Human Beings ◽  
Context Analysis ◽  
Analysis Techniques ◽  
The Right ◽  
Technological Improvements

AbstractIn the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.

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