Aspect-Oriented Recommender Systems

2013 ◽  
pp. 55-72
Author(s):  
Punam Bedi ◽  
Sumit Kr Agarwal
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Preference ◽  
Preference Learning ◽  
Decision Making Process ◽  
Design Issues ◽  
Aspect Oriented Programming ◽  
Multi Agent ◽  
Oriented Approach

Recommender systems are widely used intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. Recommender systems use the opinions of members of a community to help individuals in that community by identifying information most likely to be interesting to them or relevant to their needs. Recommender systems have various core design crosscutting issues such as: user preference learning, security, mobility, visualization, interaction etc that are required to be handled properly in order to implement an efficient, good quality and maintainable recommender system. Implementation of these crosscutting design issues of the recommender systems using conventional agent-oriented approach creates the problem of code scattering and code tangling. An Aspect-Oriented Recommender System is a multi agent system that handles core design issues of the recommender system in a better modular way by using the concepts of aspect oriented programming, which in turn improves the system reusability, maintainability, and removes the scattering and tangling problems from the recommender system.

Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics

2009 ◽  
pp. 104-124 ◽  
Author(s):  
Yu Zhang ◽  
Mark Lewis ◽  
Christine Drennon ◽  
Michael Pellon ◽  
Coleman
Keyword(s):  
Decision Making ◽  
Social Structure ◽  
Social Systems ◽  
Three Dimensional ◽  
Decision Making Process ◽  
Social Environments ◽  
Cognitive Agents ◽  
Agent Interactions ◽  
Multi Agent ◽  
Cognitive Decision

Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other diffi- culties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.

A process perspective on research and design issues in educational personalization

2019 ◽  
Vol 17 (3) ◽  
pp. 253-279 ◽  
Author(s):  
Elizabeth Brott Beese
Keyword(s):  
Decision Making ◽  
Decision Making Process ◽  
Design Issues ◽  
Personalized Education ◽  
Decision Making Processes ◽  
Educational Plans ◽  
Process Perspective ◽  
Research And Design

This article proposes that educational personalization may be usefully understood from a process perspective. It defines educational personalization as that which occurs within any planning or decision-making process which runs for one student at a time, and uses information from or about individual students, in order to output educational plans and decisions for them. Importantly, understanding personalization as a process illuminates what it is that we design when we design for personalized education (that is, planning and decision-making processes) – and helpfully suggests familiar process-analytic vocabulary (e.g. ‘trigger’, ‘agent’, ‘rules’) for describing the further designable elements of those processes. It is argued that this simple, inclusive definition forms a consensus point from which to conduct research and design conversations about personalized education. In particular, it allows for the orderly comparison of diverse designs, toward increasing our knowledge of what makes for ‘good’ personalization.

Multi-Criteria Recommender Systems

2017 ◽  
Vol 8 (4) ◽  
pp. 20-48 ◽  
Author(s):  
Ferdaous Hdioud ◽  
Bouchra Frikh ◽  
Brahim Ouhbi ◽  
Ismail Khalil
Keyword(s):  
Decision Making ◽  
Active Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Selection Method ◽  
Item Selection ◽  
Multi Criteria Decision Making ◽  
Selection Approach ◽  
Comprehensive Study

A Recommender System (RS) works much better for users when it has more information. In Collaborative Filtering, where users' preferences are expressed as ratings, the more ratings elicited, the more accurate the recommendations. New users present a big challenge for a RS, which has to providing content fitting their preferences. Generally speaking, such problems are tackled by applying Active Learning (AL) strategies that consist on a brief interview with the new user, during which she is asked to give feedback about a set selected items. This article presents a comprehensive study of the most important techniques used to handle this issue focusing on AL techniques. The authors then propose a novel item selection approach, based on Multi-Criteria ratings and a method of computing weights of criteria inspired by a multi-criteria decision making approach. This selection method is deployed to learn new users' profiles, to identify the reasons behind which items are deemed to be relevant compared to the rest items in the dataset.

Investigation About Applicability of Personal Values for Recommender System

2012 ◽  
Vol 16 (3) ◽  
pp. 404-411 ◽  
Author(s):  
Shunichi Hattori ◽  
◽  
Yasufumi Takama
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Personal Values ◽  
User Preference

A recommender systemis a fundamental technique for finding information that is likely to be preferred by users among vast amounts of information. While existing recommender systems usually employ user preference or attributes of items to make recommendations, marketing fields have been taking notice of personal values, because that such values are significantly related to user preference. This paper investigates the applicability of personal values in modeling items and users. The results of questionnaires show the feasibility of a recommender system based on personal values.

Study on Emergency Decision-Making of Natural Disaster Based on Collaboration of Multi-Agent

2013 ◽  
Vol 411-414 ◽  
pp. 2684-2693
Author(s):  
Yue E ◽  
Ye Ping Zhu
Keyword(s):  
Decision Making ◽  
Natural Disaster ◽  
Decision Making Process ◽  
Collaborative Decision Making ◽  
Agent Theory ◽  
Emergency Decision Making ◽  
Multi Agent ◽  
Emergency Decision ◽  
Decision Making Model ◽  
Collaborative Decision

Based on the occurrence and evolution of the natural disaster is characteristic of uncertainty and complexity, in this article, Agent theory and technology is applied to emergency decision-making of natural disaster in China, built the disaster emergency collaborative decision-making framework based on multi-agent, design the collaborative decision-making model, discuss the emergency decision-making mechanism based on scenario-response, through effective collaboration based on multi-agent, achieve natural disaster dynamic emergency decision-making process.

scholarly journals Question Annotation for Community Question-Answer Forums

2020 ◽  
Author(s):  
Avantika Borikar
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Decision Making Process ◽  
Knowledge Sources ◽  
Innovative Idea ◽  
Implicit And Explicit ◽  
Content Based Filtering ◽  
Filtering Approach

A decision making process is simplified with the help of recommender systems. Recommender systems process the knowledge sources and the information actively to collect data in order to build useful recommendations. These recommendations suggest suitable items to the user based on the analysis performed on the users preferences and constraints, both implicit and explicit. Using the content-based filtering approach of recommender systems, this article suggests an innovative idea to annotate the question asked by the user on a QA forum with suitable tags. This article presents a novel scheme to suggest the relevant tags by effectively analyzing questions from a clustered knowledge pool and then ranking the tags according to their relevance. This scheme aims at providing meaningful, trustworthy and persuasive recommendations which will stratify the question in the appropriate domain of a QA forum.

A Study of Intelligent Decision Supporting System Based on Multi-Agent Exercise Prescription of Psychological Disorder

2014 ◽  
Vol 926-930 ◽  
pp. 1140-1143
Author(s):  
Hui Gao ◽  
Hong Jiang Wu ◽  
Hai Yan Zhao
Keyword(s):  
Decision Making ◽  
Exercise Prescription ◽  
Psychological Disorder ◽  
Decision Making Process ◽  
Supporting System ◽  
Intelligent Decision ◽  
Decision Supporting ◽  
Decision Supporting System ◽  
Multi Agent ◽  
Technical Features

This paper combines the technical features of multi-agent to form the intelligent decision supporting system for exercise prescription of psychological disorder based on multi-agent. And studies for the system decision-making process and system implementation are also presented. Meanwhile, it shows the insufficiency of the intelligent decision supporting system to lay a foundation for the realization of computerization in exercise prescription of psychological disorder.

Environmental effects on simulated emotional and moody agents

2017 ◽  
Vol 32 ◽  
Author(s):  
Joe Collenette ◽  
Katie Atkinson ◽  
Daan Bloembergen ◽  
Karl Tuyls
Keyword(s):  
Decision Making ◽  
Small Groups ◽  
Formal Proof ◽  
The Body ◽  
Decision Making Process ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Psychological Models ◽  
Multi Agent ◽  
Large Groups

AbstractPsychological models have been used to simulate emotions within agents as part of the decision-making process. The body of this work has focussed on applying the process of decision making using emotions to social dilemmas, notably the Prisoner’s Dilemma. Previous work has focussed on agents which do not move around, with an initial analysis on how mobility and the environment can affect the decisions chosen. Additionally simulated mood has been introduced to the decision-making process. Exploring simulated emotions and mood to inform the decision-making process in multi-agent systems allows us to explore in further detail how outside influences can have an effect on different strategies. We expand and clarify aspects of how agents are affected by environmental differences. We show how emotional characters settle on an outcome without deviation by providing a formal proof. We validate how the addition of mood increases cooperation, while also showing how small groups achieve this quicker than large groups. Once pure defectors are added, to test the resilience of the cooperation achieved, we see that while agents with a low starting mood achieve a payoff closest to the pure defectors, they are reduced in numbers the most by the pure defectors.

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