Journal ArticleDOI
Interactive recommender systems
TLDR
An interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction is presented and existing interactive recommender systems are analyzed along the dimensions of the framework.Abstract:
We identify shortcomings of current recommender systems.We present an interactive recommender framework to tackle the shortcomings.We analyze existing interactive recommenders along the dimensions of our framework.Based on the analysis, we identify future research challenges and opportunities. Recommender systems have been researched extensively over the past decades. Whereas several algorithms have been developed and deployed in various application domains, recent research efforts are increasingly oriented towards the user experience of recommender systems. This research goes beyond accuracy of recommendation algorithms and focuses on various human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. In this paper, we present an interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction. Then, we analyze existing interactive recommender systems along the dimensions of our framework, including our work. Based on our survey results, we present future research challenges and opportunities.read more
Citations
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CHI '06 Extended Abstracts on Human Factors in Computing Systems
Gary M. Olson,Robin Jeffries +1 more
TL;DR: This year's extended abstracts include submissions from six different sub-communities of the human-computer interaction field: design; education; engineering; management; research and usability, as well as materials from other traditional CHI venues.
Journal ArticleDOI
A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks
TL;DR: The recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality are introduced and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.
Journal ArticleDOI
Interacting with Recommenders—Overview and Research Directions
Michael Jugovac,Dietmar Jannach +1 more
TL;DR: This work provides a comprehensive overview on the existing literature on user interaction aspects in recommender systems, covering existing approaches for preference elicitation and result presentation, as well as proposals that consider recommendation as an interactive process.
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A Survey of Matrix Completion Methods for Recommendation Systems
TL;DR: This article presents a comprehensive survey of the matrix completion methods used in recommendation systems, focusing on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues.
Proceedings ArticleDOI
Q&R: A Two-Stage Approach toward Interactive Recommendation
TL;DR: This paper explores the two stages of a single round of conversation with a user: which question to ask the user, and how to use their feedback to respond with a more accurate recommendation.
References
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Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Proceedings ArticleDOI
The eyes have it: a task by data type taxonomy for information visualizations
TL;DR: A task by data type taxonomy with seven data types and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts) is offered.
Journal ArticleDOI
Understanding and Using Context
TL;DR: An operational definition of context is provided and the different ways in which context can be used by context-aware applications are discussed, including the features and abstractions in the toolkit that make the task of building applications easier.