Decision Making and Recommendation Acceptance Issues in Recommender Systems

2012 ◽  
pp. 86-91
Author(s):  
Francesco Ricci ◽  
Giovanni Semeraro ◽  
Marco de Gemmis ◽  
Pasquale Lops
Keyword(s):  
Decision Making ◽  
Recommender Systems

scholarly journals Explaining Review-Based Recommendations: Effects of Profile Transparency, Presentation Style and User Characteristics

i-com ◽  
2020 ◽  
Vol 19 (3) ◽  
pp. 181-200
Author(s):  
Diana C. Hernandez-Bocanegra ◽  
Jürgen Ziegler
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
User Interface ◽  
Recommender Systems ◽  
Social Awareness ◽  
User Characteristics ◽  
Presentation Style ◽  
User Reviews ◽  
Decision Making Styles

Abstract Providing explanations based on user reviews in recommender systems (RS) may increase users’ perception of transparency or effectiveness. However, little is known about how these explanations should be presented to users, or which types of user interface components should be included in explanations, in order to increase both their comprehensibility and acceptance. To investigate such matters, we conducted two experiments and evaluated the differences in users’ perception when providing information about their own profiles, in addition to a summarized view on the opinions of other customers about the recommended hotel. Additionally, we also aimed to test the effect of different display styles (bar chart and table) on the perception of review-based explanations for recommended hotels, as well as how useful users find different explanatory interface components. Our results suggest that the perception of an RS and its explanations given profile transparency and different presentation styles, may vary depending on individual differences on user characteristics, such as decision-making styles, social awareness, or visualization familiarity.

Interfaces and Human Decision Making for Recommender Systems

2020 ◽  
Author(s):  
Peter Brusilovsky ◽  
Marco de Gemmis ◽  
Alexander Felfernig ◽  
Pasquale Lops ◽  
John O'Donovan ◽  
...  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Human Decision

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.

Enhancing Citizens' Participation via Recommender Systems

2020 ◽  
pp. 624-650
Author(s):  
Luis Terán
Keyword(s):  
Decision Making ◽  
Semantic Web ◽  
Private Sector ◽  
Web 2.0 ◽  
Recommender Systems ◽  
Search Engines ◽  
Information Overload ◽  
Relevant Information ◽  
Research Topic ◽  
Government Services

With the introduction of Web 2.0, which includes users as content generators, finding relevant information is even more complex. To tackle this problem of information overload, a number of different techniques have been introduced, including search engines, Semantic Web, and recommender systems, among others. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. In this chapter, the use of recommender systems on eParticipation is presented. A brief description of the eGovernment Framework used and the participation levels that are proposed to enhance participation. The highest level of participation is known as eEmpowerment, where the decision-making is placed on the side of citizens. Finally, a set of examples for the different eParticipation types is presented to illustrate the use of recommender systems.

Recsys'18 joint workshop on interfaces and human decision making for recommender systems

2018 ◽  
Author(s):  
Peter Brusilovsky ◽  
Marco de Gemmis ◽  
Alexander Felfernig ◽  
Pasquale Lops ◽  
John O'Donovan ◽  
...  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Human Decision ◽  
Joint Workshop

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.

RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

2016 ◽  
Author(s):  
Peter Brusilovsky ◽  
Alexander Felfernig ◽  
Pasquale Lops ◽  
John O'Donovan ◽  
Giovanni Semeraro ◽  
...  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Human Decision ◽  
Joint Workshop

RecSys'12 workshop on human decision making in recommender systems

2012 ◽  
Author(s):  
Marco de Gemmis ◽  
Alexander Felfernig ◽  
Pasquale Lops ◽  
Francesco Ricci ◽  
Giovanni Semeraro ◽  
...  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Human Decision

scholarly journals User Modeling from Review Browsing History for Personal Values-Based Recommendation

2020 ◽  
Vol 24 (3) ◽  
pp. 326-334
Author(s):  
Yasufumi Takama ◽  
◽  
Suzuto Shimizu
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
User Modeling ◽  
Personal Values ◽  
User Model ◽  
User Feedback ◽  
Modeling Method ◽  
History Of

This paper proposes a personal values-based user modeling method from user’s browsing history of reviews. Personal values-based user modeling and its application to recommender systems have been studied. This approach models users’ personal values as the effect of item’s attributes on their decision making. While existing method obtains a user model from reviews posted by a user, this paper proposes to obtain it from reviews a user consulted for his/her decision making. Methods for determining reviews to present for obtaining user feedback, as well as for selecting items to recommend are proposed, of which effectiveness are shown with user experiments.

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