Improving the User Experience with a Conversational Recommender System

2018 ◽  
pp. 528-538 ◽  
Author(s):  
Fedelucio Narducci ◽  
Marco de Gemmis ◽  
Pasquale Lops ◽  
Giovanni Semeraro
Keyword(s):  
User Experience ◽  
Recommender System

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.

Challenges and Applications of Recommender Systems in E-Commerce

2021 ◽  
pp. 175-188
Author(s):  
Taushif Anwar ◽  
V. Uma ◽  
Md Imran Hussain
Keyword(s):  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
Recommendation System ◽  
Main Idea ◽  
Important Task ◽  
Significant Challenge ◽  
Online Business ◽  
The One ◽  
The Relationship

E-commerce and online business are getting too much attention and popularity in this era. A significant challenge is helping a customer through the recommendation of a big list of items to find the one they will like the most efficiently. The most important task of a recommendation system is to improve user experience through the most relevant recommendation of items based on their past behaviour. In e-commerce, the main idea behind the recommender system is to establish the relationship between users and items to recommend the most relevant items to the particular user. Most of the e-commerce websites such as Amazon, Flipkart, E-Bay, etc. are already applying the recommender system to assist their users in finding appropriate items. The main objective of this chapter is to illustrate and examine the issues, attacks, and research applications related to the recommender system.

scholarly journals Improving User Experience for Recommender System using Diversity

2017 ◽  
Vol 24 (1) ◽  
pp. 95-115
Author(s):  
Yoshifumi Seki ◽  
Yoshinori Fukushima ◽  
Koji Yoshida ◽  
Yutaka Matsuo
Keyword(s):  
User Experience ◽  
Recommender System

scholarly journals Evaluating Recommender System Stability with Influence-Guided Fuzzing

2019 ◽  
Vol 33 ◽  
pp. 4934-4942
Author(s):  
David Shriver ◽  
Sebastian Elbaum ◽  
Matthew B. Dwyer ◽  
David S. Rosenblum
Keyword(s):  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
Personal Experience ◽  
System Stability

Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender’s stability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset. We find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches.

A genetic algorithm approach to recommender system cold start problem.

2021 ◽  
Author(s):  
Sanjeevan Sivapalan
Keyword(s):  
Genetic Algorithm ◽  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
Evaluation Method ◽  
New Products ◽  
Cold Start ◽  
Genetic Algorithm Approach ◽  
Cold Start Problem ◽  
Novel Algorithm

Recommender systems (RS) are ubiquitous and used in many systems to augment user experience to improve usability and they achieve this by helping users discover new products to consume. They, however, suffer from cold-start problem which occurs when there is not enough information to generate recommendations to a user. Cold-start occurs when a new user enters the system that we don’t know about. We have proposed a novel algorithm to make recommendations to new users by recommending outside of their preferences. We also propose a genetic algorithm based solution to make recommendations when we lack information about user and a transitive algorithm to form neighbourhood. Altogether, we developed three algorithms and tested them using they MovieLens dataset. We have found that all of our algorithms performed well during our testing using the offline-evaluation method.

Assessing User Experience with Crutch Use

2011 ◽  
Author(s):  
Christina Harrington ◽  
Sharon Joines
Keyword(s):  
User Experience
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