Fuzzy cognitive agents for personalized recommendation

2003 ◽  
Author(s):  
Chunyan Miao ◽  
Qiang Yang ◽  
Haijing Fang ◽  
A. Goh
Keyword(s):  
Personalized Recommendation ◽  
Cognitive Agents

The World is not Enough: Trust in Cognitive Agents

2012 ◽  
Author(s):  
Ewart J. de Visser ◽  
Frank Krueger ◽  
Patrick McKnight ◽  
Steven Scheid ◽  
Melissa Smith ◽  
...  
Keyword(s):  
Cognitive Agents ◽  
The World

Personalized recommendation via network-based inference with time

2020 ◽  
Vol 550 ◽  
pp. 123917
Author(s):  
Yang Wang ◽  
Lixin Han
Keyword(s):  
Personalized Recommendation

An interpretable mechanism for personalized recommendation based on cross feature

2021 ◽  
pp. 1-12
Author(s):  
Lv YE ◽  
Yue Yang ◽  
Jian-Xu Zeng
Keyword(s):  
Recommender System ◽  
Online Shopping ◽  
Side Information ◽  
Attention Mechanism ◽  
Personalized Recommendation ◽  
Model Based ◽  
Embedded Model ◽  
Feature Attention ◽  
Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm

2021 ◽  
pp. 016555152098549
Author(s):  
Donghee Shin
Keyword(s):  
Artificial Intelligence ◽  
Embodied Cognition ◽  
Cognitive Processes ◽  
Process Model ◽  
Extended Cognition ◽  
Dual Process ◽  
Personalized Recommendation ◽  
Normative Values ◽  
And Performance ◽  
Concise Guide

The recent proliferation of artificial intelligence (AI) gives rise to questions on how users interact with AI services and how algorithms embody the values of users. Despite the surging popularity of AI, how users evaluate algorithms, how people perceive algorithmic decisions, and how they relate to algorithmic functions remain largely unexplored. Invoking the idea of embodied cognition, we characterize core constructs of algorithms that drive the value of embodiment and conceptualizes these factors in reference to trust by examining how they influence the user experience of personalized recommendation algorithms. The findings elucidate the embodied cognitive processes involved in reasoning algorithmic characteristics – fairness, accountability, transparency, and explainability – with regard to their fundamental linkages with trust and ensuing behaviors. Users use a dual-process model, whereby a sense of trust built on a combination of normative values and performance-related qualities of algorithms. Embodied algorithmic characteristics are significantly linked to trust and performance expectancy. Heuristic and systematic processes through embodied cognition provide a concise guide to its conceptualization of AI experiences and interaction. The identified user cognitive processes provide information on a user’s cognitive functioning and patterns of behavior as well as a basis for subsequent metacognitive processes.

Developing a personalized recommendation system in a smart product service system based on unsupervised learning model

2021 ◽  
Vol 128 ◽  
pp. 103421
Author(s):  
Ming-Chuan Chiu ◽  
Jih-Hung Huang ◽  
Saraj Gupta ◽  
Gulsen Akman
Keyword(s):  
Unsupervised Learning ◽  
Recommendation System ◽  
Service System ◽  
Learning Model ◽  
Personalized Recommendation ◽  
Product Service System ◽  
Product Service ◽  
Smart Product
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