TrustTF: A tensor factorization model using user trust and implicit feedback for context-aware recommender systems

2020 ◽  
Vol 209 ◽  
pp. 106434
Author(s):  
Jianli Zhao ◽  
Wei Wang ◽  
Zipei Zhang ◽  
Qiuxia Sun ◽  
Huan Huo ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Implicit Feedback ◽  
Context Aware ◽  
Tensor Factorization ◽  
Factorization Model ◽  
User Trust

Fast Tensor Factorization for Large-Scale Context-Aware Recommendation from Implicit Feedback

2020 ◽  
Vol 6 (1) ◽  
pp. 201-208 ◽  
Author(s):  
Szu-Yu Chou ◽  
Jyh-Shing Roger Jang ◽  
Yi-Hsuan Yang
Keyword(s):  
Large Scale ◽  
Implicit Feedback ◽  
Context Aware ◽  
Tensor Factorization

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

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.

Using Topic Hierarchies with Privileged Information to Improve Context-Aware Recommender Systems

2014 ◽  
Author(s):  
Camila V. Sundermann ◽  
Marcos A. Domingues ◽  
Ricardo M. Marcacini ◽  
Solange O. Rezende
Keyword(s):  
Recommender Systems ◽  
Context Aware ◽  
Privileged Information

scholarly journals Enhanced tensor factorization framework using non-negative and probabilistic tensor factorization approaches for microblogging content propagation modelling

2017 ◽  
Vol 7 (1.1) ◽  
pp. 88
Author(s):  
N. Baggyalakshmi ◽  
A. Kavitha ◽  
A. Marimuthu
Keyword(s):  
Gradient Descent ◽  
User Behavior ◽  
Complete System ◽  
Propagation Model ◽  
Tensor Factorization ◽  
Propagation Modeling ◽  
Factorization Model ◽  
Factorization Structure ◽  
Linguistic Patterns ◽  
Fast Gradient

With the aim of identifying the user’s preferences, Content propagation modeling from the micro-blogging sites aids diverse organizations. In existing studies, four user behavior aspects were used by the content propagation model that is to say topic virality, user’s position, user susceptibility and user virality. The propagation occurrences are signified as a tensor factorization model so-called V2S is presented with the aim of deriving the behavioral aspects via which the content propagation is designed. On the other hand, it doesn’t comprise the linguistic patterns in the content that decreases the performance of the content propagation. Moreover, by utilizing advanced tensor approaches, the factorization structure is improved. Therefore the performance of the complete system is decreased meaningfully. With the aim of overcoming the aforesaid problems, Enhanced V2S (EV2S) Tensor Factorization framework is presented in this research that make use of the Probabilistic Latent Tensor Factorization (PLTF) as well as Non-negative Tensor Factorization (NTF) in order to derive the behavioral facets. NTF is presented for decreasing the content propagation errors. By making use of fast gradient descent technique, the unrestrained issue, which happens in this model is solved. This research system identifies the reposts as well as re-tweets in huge datasets proficiently with minimum processing time. From the experimentation outcomes, it is proved that the EV2S-PLTF tensor factorization performs better when compared to the previous tensor frameworks.

scholarly journals Context-aware Music Recommender System Based on Implicit Feedback

2021 ◽  
Vol 36 (1) ◽  
pp. WI2-D_1-10
Author(s):  
Yasufumi Takama ◽  
Jing-cheng Zhang ◽  
Hiroki Shibata
Keyword(s):  
Recommender System ◽  
Implicit Feedback ◽  
Context Aware
Sign in / Sign up

Export Citation Format

Share Document