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Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches

https://doi.org/10.3390/su13116165
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-44593-5_25openalex-snapshot
Similarity vs. Diversity
inferredcites →https://doi.org/10.1007/3-540-45006-8_23openalex-snapshot
On the Role of Diversity in Conversational Recommender Systems
inferredcites →https://doi.org/10.1007/978-0-387-85820-3openalex-snapshot
Recommender Systems Handbook
inferredcites →https://doi.org/10.1007/978-0-387-85820-3_1openalex-snapshot
Introduction to Recommender Systems Handbook
inferredcites →https://doi.org/10.1007/978-0-387-85820-3_8openalex-snapshot
Evaluating Recommendation Systems
inferredcites →https://doi.org/10.1007/978-1-4899-7637-6openalex-snapshot
Recommender Systems Handbook
inferredcites →https://doi.org/10.1007/978-1-4899-7637-6_26openalex-snapshot
Novelty and Diversity in Recommender Systems
inferredcites →https://doi.org/10.1007/978-3-319-14717-8_15openalex-snapshot
A Reputation-Enhanced Recommender System
inferredcites →https://doi.org/10.1007/978-3-319-29659-3openalex-snapshot
Recommender Systems: The Textbook
inferredcites →https://doi.org/10.1007/s00607-018-0687-5openalex-snapshot
How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm
inferredcites →https://doi.org/10.1007/s10462-018-9654-yopenalex-snapshot
A review on deep learning for recommender systems: challenges and remedies
inferredcites →https://doi.org/10.1007/s10726-007-9082-xopenalex-snapshot
Exploring usage continuance of e-negotiation systems: expectation and disconfirmation approach
inferredcites →https://doi.org/10.1007/s11257-012-9135-yopenalex-snapshot
Comparing context-aware recommender systems in terms of accuracy and diversity
inferredcites →https://doi.org/10.1007/s13042-017-0762-9openalex-snapshot
How good your recommender system is? A survey on evaluations in recommendation
inferredcites →https://doi.org/10.1016/j.chb.2015.03.057openalex-snapshot
Switching behavior and customer satisfaction in mobile services: Analyzing virtual and traditional operators
inferredcites →https://doi.org/10.1016/j.cie.2019.02.028openalex-snapshot
An integrated recommender system for improved accuracy and aggregate diversity
inferredcites →https://doi.org/10.1016/j.dss.2007.04.003openalex-snapshot
A temporal approach to expectations and desires from knowledge management systems
inferredcites →https://doi.org/10.1016/j.dss.2015.03.008openalex-snapshot
Recommender system application developments: A survey
inferredcites →https://doi.org/10.1016/j.eswa.2012.02.038openalex-snapshot
A literature review and classification of recommender systems research
inferredcites →https://doi.org/10.1016/j.ijhcs.2006.01.003openalex-snapshot
Understanding e-learning continuance intention: An extension of the Technology Acceptance Model
inferredcites →https://doi.org/10.1016/j.ijinfomgt.2009.09.006openalex-snapshot
A group recommendation system for online communities
inferredcites →https://doi.org/10.1016/j.ijinfomgt.2016.01.005openalex-snapshot
Collaborative filtering with facial expressions for online video recommendation
inferredcites →https://doi.org/10.1016/j.ins.2007.02.036openalex-snapshot
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
inferredcites →https://doi.org/10.1016/j.ipm.2020.102459openalex-snapshot
Accuracy-diversity trade-off in recommender systems via graph convolutions
inferredcites →https://doi.org/10.1016/j.knosys.2013.03.012openalex-snapshot
Recommender systems survey
inferredcites →https://doi.org/10.1016/j.knosys.2020.105798openalex-snapshot
Pair-wise Preference Relation based Probabilistic Matrix Factorization for Collaborative Filtering in Recommender System
inferredcites →https://doi.org/10.1016/j.neunet.2014.09.003openalex-snapshot
Deep learning in neural networks: An overview
inferredcites →https://doi.org/10.1016/s0957-4174(02)00052-0openalex-snapshot
A personalized recommender system based on web usage mining and decision tree induction
inferredcites →https://doi.org/10.1016/s0957-4174(03)00138-6openalex-snapshot
Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce
inferredcites →https://doi.org/10.1073/pnas.1000488107openalex-snapshot
Solving the apparent diversity-accuracy dilemma of recommender systems
inferredcites →https://doi.org/10.1080/14783360701231302openalex-snapshot
The Impact of Website Quality Dimensions on Customer Satisfaction in the B2C E-commerce Context
inferredcites →https://doi.org/10.1089/cyber.2009.0236openalex-snapshot
Applying the Expectancy Disconfirmation and Regret Theories to Online Consumer Behavior
inferredcites →https://doi.org/10.1109/access.2018.2877208openalex-snapshot
A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks
inferredcites →https://doi.org/10.1109/mic.2003.1167344openalex-snapshot
Amazon.com recommendations: item-to-item collaborative filtering
inferredcites →https://doi.org/10.1109/tkde.2011.15openalex-snapshot
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
inferredcites →https://doi.org/10.1109/tsc.2009.7openalex-snapshot
Personalized Recommendation over a Customer Network for Ubiquitous Shopping
inferredcites →https://doi.org/10.1109/tsmca.2011.2147306openalex-snapshot
Customer-Driven Content Recommendation Over a Network of Customers
inferredcites →https://doi.org/10.1145/1060745.1060754openalex-snapshot
Improving recommendation lists through topic diversification
inferredcites →https://doi.org/10.1145/1125451.1125659openalex-snapshot
Being accurate is not enough
inferredcites →https://doi.org/10.1145/1242572.1242610openalex-snapshot
Google news personalization
inferredcites →https://doi.org/10.1145/138859.138867openalex-snapshot
Using collaborative filtering to weave an information tapestry
inferredcites →https://doi.org/10.1145/1835449.1835486openalex-snapshot
Temporal diversity in recommender systems
inferredcites →https://doi.org/10.1145/1864708.1864801openalex-snapshot
Context-awareness in recommender systems
inferredcites →https://doi.org/10.1145/1944339.1944341openalex-snapshot
Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation
inferredcites →https://doi.org/10.1145/2043932.2043962openalex-snapshot
A user-centric evaluation framework for recommender systems
inferredcites →https://doi.org/10.1145/2532508.2532512openalex-snapshot
Research paper recommender system evaluation
inferredcites →https://doi.org/10.1145/2792838.2800180openalex-snapshot
Blockbusters and Wallflowers
inferredcites →https://doi.org/10.1145/2926720openalex-snapshot
Diversity, Serendipity, Novelty, and Coverage
inferredcites →https://doi.org/10.1145/2959100.2959190openalex-snapshot
Deep Neural Networks for YouTube Recommendations
inferredcites →https://doi.org/10.1145/2988450.2988454openalex-snapshot
Wide & Deep Learning for Recommender Systems
inferredcites →https://doi.org/10.1145/3038912.3052569openalex-snapshot
Neural Collaborative Filtering
inferredcites →https://doi.org/10.1145/3097983.3098108openalex-snapshot
Embedding-based News Recommendation for Millions of Users
inferredcites →https://doi.org/10.1145/3109859.3109896openalex-snapshot
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
inferredcites →https://doi.org/10.1145/3132847.3133154openalex-snapshot
Deep Neural Networks for News Recommendations
inferredcites →https://doi.org/10.1145/3158369openalex-snapshot
Facial Expression Analysis under Partial Occlusion
inferredcites →https://doi.org/10.1145/3219819.3219846openalex-snapshot
Reinforcement Learning to Rank in E-Commerce Search Engine
inferredcites →https://doi.org/10.1145/3340631.3394858openalex-snapshot
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation
inferredcites →https://doi.org/10.1145/3343117openalex-snapshot
Joint Neural Collaborative Filtering for Recommender Systems
inferredcites →https://doi.org/10.1145/3364544.3364830openalex-snapshot
CHARS
inferredcites →https://doi.org/10.1145/3386243openalex-snapshot
Context-Aware Recommendations Based on Deep Learning Frameworks
inferredcites →https://doi.org/10.1145/358916.358995openalex-snapshot
Explaining collaborative filtering recommendations
inferredcites →https://doi.org/10.1145/371920.372071openalex-snapshot
Item-based collaborative filtering recommendation algorithms
inferredcites →https://doi.org/10.1145/963770.963772openalex-snapshot
Evaluating collaborative filtering recommender systems
inferredcites →https://doi.org/10.1287/isre.13.3.296.76openalex-snapshot
The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach
inferredcites →https://doi.org/10.1561/9781601984432openalex-snapshot
Collaborative Filtering Recommender Systems
inferredcites →https://doi.org/10.1609/aimag.v32i3.2364openalex-snapshot
Context‐Aware Recommender Systems
inferredcites →https://doi.org/10.2307/20721420openalex-snapshot
What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.Com1
inferredcites →https://doi.org/10.2307/3150499openalex-snapshot
A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions
inferredcites →https://doi.org/10.2307/3250921openalex-snapshot
Understanding Information Systems Continuance: An Expectation-Confirmation Model1
inferredcites →https://doi.org/10.2753/mis0742-1222230303openalex-snapshot
Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings
inferredcites →https://doi.org/10.3390/app10041257openalex-snapshot
Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
inferredcites →https://doi.org/10.3390/su10010147openalex-snapshot
Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business
inferredcites →https://doi.org/10.3390/su12030969openalex-snapshot
A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1707.07435openalex-snapshot
Deep Learning based Recommender System: A Survey and New Perspectives
inferredcites →https://doi.org/10.48550/arxiv.1803.00710openalex-snapshot
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
inferredcites →https://doi.org/10.5220/0006370007520759openalex-snapshot
A Context Aware Approach for Promoting Tourism Events: The Case of Artist’s Lights in Salerno

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Retention Induced Biases in a Recommendation System with Heterogeneous Users
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Emotional Resonance or Rational Guidance? Exploring the Mechanisms Through Which Generative AI Recommendation Roles Shape Consumers' Adoption Intentions
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Comparative Analysis: Recommendation Techniques in E-Commerce
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Multichannel Consumer Service Integration of Recommendation Systems
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Retention Induced Biases in a Recommendation System with Heterogeneous Users
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Adaptation of AI and AR/VR Technology in Omnichannel Strategy to Strengthen Competitiveness in the E-commerce Sector
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GoRS - A Neuro-Symbolic, User-Centric, and Goal-Oriented Recommendation System for DIY-Projects
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Combining Accuracy and Diversity to Enhance the Quality of the Recommender System
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Maximizing diversity in k-pattern set mining through constraint programming and entropy
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Enhancing user information disclosure intention in dynamic conversations of intelligent recommendation systems based on large language models: A perspective of user gratification and privacy calculus
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SFNN: A secure and diverse recommender system through graph neural network and regularized variational autoencoder
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A Model for the Evaluation of User Satisfaction with Recommendation Systems of Entertainment Platforms
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Am I watching or being watched? Exploring the selective disclosure paradox in users’ self-censorship to dataveillance awareness in video recommender systems
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Impact of user reviews and ratings on overall rating in Indian tourism hotels
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Beyond Accuracy: How Algorithm Characteristics and Digital Generational Differences Affect User Interaction and Acceptance in Fashion Recommendation Systems
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Reliance on AI-Driven Recommendations on Netflix: An Integration of the Heuristics-Systematic Model with Human Agency Factors
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Investigating the drivers of discontinuous usage in short-video platforms: the role of information characteristics
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Impact on recommendation performance of online review helpfulness and consistency
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Transforming music listeners to subscribers: music recommendation system’s impact on customer subscription intention with the mediating role of customer experience
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The influence of data-driven personalised fashion recommendations on consumer decision-making
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Analyzing the Impact of Components of Yelp.com on Recommender System Performance: Case of Austin
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Hybrid Collaborative Movie Recommendation System
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Knowledge Transfer In Cross Domain Recommender Systems Using Deep Latent Embeddings
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Refining machine learning evaluation : statistical insights into model performance and fairness
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.