scholarly journals Towards a Novel User Satisfaction Modelling for Museum Visit Recommender Systems

2018 ◽  
pp. 60-75 ◽  
Author(s):  
George Pavlidis
Keyword(s):  
Recommender Systems ◽  
User Satisfaction ◽  
Museum Visit

scholarly journals An Approach to Integrating Sentiment Analysis into Recommender Systems

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5666
Author(s):  
Cach N. Dang ◽  
María N. Moreno-García ◽  
Fernando De la Prieta
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
User Satisfaction ◽  
Online News ◽  
Learning Models ◽  
Adaptive Architecture ◽  
Wide Range ◽  
The One

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

Item Familiarity as a Possible Confounding Factor in User-Centric Recommender Systems Evaluation

i-com ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Dietmar Jannach ◽  
Lukas Lerche ◽  
Michael Jugovac
Keyword(s):  
Recommender Systems ◽  
User Satisfaction ◽  
User Study ◽  
Predictive Accuracy ◽  
Confounding Factor ◽  
Academic Research ◽  
Strongly Correlated ◽  
Quality Factors ◽  
User Centric ◽  
Assess Quality

AbstractUser studies play an important role in academic research in the field of recommender systems as they allow us to assess quality factors other than the predictive accuracy of the underlying algorithms. User satisfaction is one such factor that is often evaluated in laboratory settings and in many experimental designs one task of the participants is to assess the suitability of the system-generated recommendations. The effort required by the user to make such an assessment can, however, depend on the user’s familiarity with the presented items and directly impact on the reported user satisfaction. In this paper, we report the results of a preliminary recommender systems user study using Mechanical Turk, which indicates that item familiarity is strongly correlated with overall satisfaction.

scholarly journals The effect of visualisation on user experience in recommender systems

2021 ◽  
Vol 26 (3) ◽  
Author(s):  
Wen-Yau Liang ◽  
Chun-Che Huang ◽  
Tzu-Liang Tseng ◽  
Zih-Yan Wang ◽  
◽  
...  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
User Satisfaction ◽  
Business Environment ◽  
Perceived Usefulness ◽  
Continuance Intention ◽  
Systems Research ◽  
Proposed Model

Introduction. Measuring user experience, though natural in a business environment, is often challenging for recommender systems research. How recommender systems can substantially improve consumers’ decision making is well understood; but the influence of specific design attributes of the recommender system interface on decision making and other outcome measures is far less understood. Method. This study provides the first empirical test of post-acceptance model adaption for information system continuance in the context of recommender systems. Based on the proposed model, two presentation types (with or without using tag cloud) are compared. An experimental design is used and a questionnaire is developed to analyse the data. Analysis. Data were analysed using SPSS and SmartPLS (partial least squares path modeling method). Statistical methods used for the questionnaire on user satisfaction were a reliability analysis, a validity analysis and T-tests. Results. The results demonstrate that the proposed model is supported and that the visual recommender system can indeed significantly enhance user satisfaction and continuance intention. Conclusions. In order to improve the satisfaction or continuance intention of users, it is required to improve the perceived usefulness, effectiveness and visual attractiveness of a recommender system.

A Characterisation and Framework for User-Centric Factors in Evaluation Methods for Recommender Systems

2017 ◽  
Vol 6 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Tatenda D. Kavu ◽  
Kuda Dube ◽  
Peter G. Raeth ◽  
Gilford T. Hapanyengwi
Keyword(s):  
Conceptual Framework ◽  
Recommender Systems ◽  
User Experience ◽  
User Satisfaction ◽  
Optimal Solution ◽  
User Interaction ◽  
Evaluation Methods ◽  
User Preferences ◽  
Evaluation Framework ◽  
User Centric

Researchers have worked on-finding e-commerce recommender systems evaluation methods that contribute to an optimal solution. However, existing evaluations methods lack the assessment of user-centric factors such as buying decisions, user experience and user interactions resulting in less than optimum recommender systems. This paper investigates the problem of adequacy of recommender systems evaluation methods in relation to user-centric factors. Published work has revealed limitations of existing evaluation methods in terms of evaluating user satisfaction. This paper characterizes user-centric evaluation factors and then propose a user-centric evaluation conceptual framework to identify and expose a gap within literature. The researchers used an integrative review approach to formulate both the characterization and the conceptual framework for investigation. The results reveal a need to come up with a holistic evaluation framework that combines system-centric and user-centric evaluation methods as well as formulating computational user-centric evaluation methods. The conclusion reached is that, evaluation methods for e-commerce recommender systems lack full assessment of vital factors such as: user interaction, user experience and purchase decisions. A full consideration of these factors during evaluation will give birth to new types of recommender systems that predict user preferences using user decision-making process profiles, and that will enhance user experience and increase revenue in the long run.

State-of-the-Art Recommender Systems

2011 ◽  
pp. 1-22 ◽  
Author(s):  
Laurent Candillier ◽  
Kris Jack ◽  
Françoise Fessant ◽  
Frank Meyer
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Hybrid System ◽  
User Satisfaction ◽  
State Of The Art ◽  
Predictive Performance ◽  
Performance Accuracy ◽  
Item Content ◽  
User Ratings ◽  
Content Based Filtering

The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative filtering approaches can be distinguished from content-based ones. The former is based on a set of user ratings on items, while the latter uses item content descriptions and user thematic profiles. While collaborative filtering systems often result in better predictive performance, content-based filtering offers solutions to the limitations of collaborative filtering, as well as a natural way to interact with users. These complementary approaches thus motivate the design of hybrid systems. In this chapter, the main algorithmic methods used for recommender systems are presented in a state of the art. The evaluation of recommender systems is currently an important issue. The authors focus on two kinds of evaluations. The first one concerns the performance accuracy: several approaches are compared through experiments on two real movies rating datasets MovieLens and Netflix. The second concerns user satisfaction and for this a hybrid system is implemented and tested with real users.

User Personality and User Satisfaction with Recommender Systems

2017 ◽  
Vol 20 (6) ◽  
pp. 1173-1189 ◽  
Author(s):  
Tien T. Nguyen ◽  
F. Maxwell Harper ◽  
Loren Terveen ◽  
Joseph A. Konstan
Keyword(s):  
Recommender Systems ◽  
User Satisfaction ◽  
User Personality

scholarly journals A Study of User Intervention and User Satisfaction in Recommender Systems

2014 ◽  
Vol 22 (4) ◽  
pp. 669-678
Author(s):  
Yoshinori Hijikata ◽  
Yuki Kai ◽  
Shogo Nishida
Keyword(s):  
Recommender Systems ◽  
User Satisfaction ◽  
User Intervention

scholarly journals An Approach to Integrating Sentiment Analysis into Recommender Systems

2021 ◽  
Author(s):  
Cach Nhan Dang ◽  
María N. Moreno ◽  
Fernando De la Prieta
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
User Satisfaction ◽  
Online News ◽  
Learning Models ◽  
Adaptive Architecture ◽  
Wide Range

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

scholarly journals Exploiting a Map-Based Interface in Conversational Recommender Systems for Mobile Travelers

2010 ◽  
pp. 73-93 ◽  
Author(s):  
Francesco Ricci ◽  
Quang Nhat Nguyen ◽  
Olga Averjanova
Keyword(s):  
Recommender Systems ◽  
Web Sites ◽  
User Satisfaction ◽  
User Study ◽  
Information Overload ◽  
Mobile Internet ◽  
Amount Of Information ◽  
Travel And Tourism ◽  
Related Information ◽  
Usage Context

Nowadays travel and tourism Web sites store and offer a large volume of travel related information and services. Furthermore, this huge amount of information can be easily accessed using mobile devices, such as a phone with mobile Internet connection capability. However, this information can easily overwhelm a user because of the large number of information items to be shown and the limited screen size in the mobile device. Recommender systems (RSs) are often used in conjunction with Web tools to effectively help users in accessing this overwhelming amount of information. These recommender systems can support the user in making a decision even when specific knowledge necessary to autonomously evaluate the offerings is not available. Recommender systems cope with the information overload problem by providing a user with personalized recommendations (i.e., a well chosen selection of the items contained in the repository), adapting this selection to the user’s needs and preferences in a particular usage context. In this chapter, the authors present a recommendation approach integrating a conversational preference acquisition technology based on “critiquing” with map visualization technologies to build a new map-based conversational mobile RS that can effectively and intuitively support travelers in finding their desired products and services. The results of the authors’ real-user study show that integrating map-based visualization and critiquing-based interaction in mobile RSs improves the system’s recommendation effectiveness, and increases the user satisfaction.

scholarly journals Recommender systems to support learners’ Agency in a Learning Context: a systematic review

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Michelle Deschênes
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
User Satisfaction ◽  
Learning Performance ◽  
Learning Goals ◽  
Future Research ◽  
Technology Enhanced Learning ◽  
Learning Context ◽  
Policy And Practice ◽  
Evidence For Policy

Abstract Recommender systems for technology-enhanced learning are examined in relation to learners’ agency, that is, their ability to define and pursue learning goals. These systems make it easier for learners to access resources, including peers with whom to learn and experts from whom to learn. In this systematic review of the literature, we apply an Evidence for Policy and Practice Information (EPPI) approach to examine the context in which recommenders are used, the manners in which they are evaluated and the results of those evaluations. We use three databases (two in education and one in applied computer science) and retained articles published therein between 2008 and 2018. Fifty-six articles meeting the requirements for inclusion are analyzed to identify their approach (content-based, collaborative filtering, hybrid, other) and the experiment settings (accuracy, user satisfaction or learning performance), as well as to examine the results and the manner in which they were presented. The results of the majority of the experiments were positive. Finally, given the results introduced in this systematic review, we identify future research questions.

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