Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey

Summary

Biba et al. (2017) surveyed visual data mining techniques used for collaborative filtering, covering visual data mining taxonomies and tools, the three main kinds of collaborative filtering (memory-based, model-based and hybrid) and visual collaborative filtering systems evaluated on EachMovie, MovieLens and Netflix data. The authors conclude that combining visual data mining techniques with collaborative filtering has led to substantial improvement of the overall systems and can alleviate the shortcomings of traditional collaborative filtering.

Practical implication. Data analysts and users should use visual data mining alongside traditional data mining so that they can visually explore and interpret patterns in recommender data.

The Problem

Collaborative filtering recommender systems suffer from sparsity and opacity, and the potential of visual data mining to address these shortcomings had not been surveyed.

The Methodology

State-of-the-art literature survey of visual data mining taxonomies and tools, memory-based, model-based and hybrid collaborative filtering, and visual collaborative filtering systems evaluated on EachMovie, MovieLens and Netflix data.

The Core Finding

The survey organizes visual data mining techniques, tools and collaborative filtering approaches, and concludes that combining visual data mining with collaborative filtering has led to substantial improvement and can alleviate the shortcomings of traditional collaborative filtering.

The Citation

Biba, M., Vajjhala, N. R., & Nishani, L. (2017). Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey. In Collaborative Filtering Using Data Mining and Analysis (pp. 217–235). IGI Global. https://doi.org/10.4018/978-1-5225-0489-4.ch012 Copy APA

The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the chapter, written from the published abstract and text. The official abstract and citation details are given further down.

The chapter in detail

What question does this chapter answer?

What visual data mining techniques and tools exist, what are the main collaborative filtering approaches, and how has visual data mining been applied to collaborative filtering recommender systems?

What did the chapter find, in detail?

The chapter surveys visual data mining taxonomies (for example Keim’s six classes: graph-based, geometric projection, icon-based, pixel-based, hierarchical and hybrid), popular tools (Clementine, TimeSearcher, ThemeRiver, VizTree, XmdvTool), and the three main kinds of collaborative filtering (memory-based, model-based and hybrid). Reviewing visual collaborative filtering systems such as NEAR, dependency networks, minimum spanning dendrograms and PCA/CCA item maps tested on MovieLens and Netflix data, it concludes that combining visual data mining with collaborative filtering has led to substantial improvement and can alleviate the shortcomings of traditional collaborative filtering.

Why does it matter?

Visual data mining augments, rather than replaces, traditional data mining by bringing the data analyst into the exploration process through visual interpretation of large datasets. For recommender systems, the authors conclude that visual techniques help make sense of what is happening between users in collaborative filtering systems and address problems such as sparse data, scalability and new users or items.

Methods and data

Definitions and key terms

Visual data mining

The chapter’s definition: visual data mining combines data mining methods and computer-aided, interactive visual techniques to discover novel and interpretable patterns with the help of human perception abilities. — as used in Biba et al. (2017)

Collaborative filtering

The chapter’s definition: collaborative filtering allows recommendations to users taking into account how other users have rated items, using a database of user ratings over items. — as used in Biba et al. (2017)

Recommender systems

The chapter’s definition: software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. — as used in Biba et al. (2017)

All key terms across the publications →

Abstract

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Abstract as published in Collaborative Filtering Using Data Mining and Analysis.

Practical implications

• For data analysts, visual data mining augments rather than replaces traditional data mining, bringing the analyst into the exploration process through visual interpretation of large datasets.
• Researchers working on collaborative filtering and recommender system design can use visual techniques to make sense of what is happening between users and to address problems such as sparse data, scalability and new users or items.
• Researchers choosing memory-based collaborative filtering gain an approach that is easy to implement and performs well on dense datasets, but it depends on user ratings, deteriorates on sparse data and requires more memory than model-based techniques.

Drawn from Biba et al. (2017), In Collaborative Filtering Using Data Mining and Analysis, pp. 217–235, IGI Global.

When this research may be relevant

This chapter may be relevant to researchers surveying visual data mining and information visualisation techniques, collaborative filtering and recommender system design, and the combination of visual analytics with recommendation algorithms.

Research topics addressed: visual data mining; collaborative filtering; recommender systems; data visualization; information visualization; data mining; memory-based collaborative filtering; model-based collaborative filtering; hybrid collaborative filtering; content-based filtering; visual analytics; movie recommendation; state-of-the-art survey

How to cite

APA 7

Biba, M., Vajjhala, N. R., & Nishani, L. (2017). Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey. In Collaborative Filtering Using Data Mining and Analysis (pp. 217–235). IGI Global. https://doi.org/10.4018/978-1-5225-0489-4.ch012Copy APA citation

MLA 9

Biba, Marenglen, et al. “Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey.” Collaborative Filtering Using Data Mining and Analysis, IGI Global, 2017, pp. 217–35. https://doi.org/10.4018/978-1-5225-0489-4.ch012.Copy MLA citation

IEEE

M. Biba, N. R. Vajjhala, and L. Nishani, “Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey,” in Collaborative Filtering Using Data Mining and Analysis. IGI Global, 2017, pp. 217–235, doi: 10.4018/978-1-5225-0489-4.ch012.Copy IEEE citation

@incollection{biba2017visual,
  title = {Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey},
  author = {Biba, Marenglen and Vajjhala, Narasimha Rao and Nishani, Lediona},
  booktitle = {Collaborative Filtering Using Data Mining and Analysis},
  pages = {217--235},
  year = {2017},
  publisher = {IGI Global},
  doi = {10.4018/978-1-5225-0489-4.ch012},
  url = {https://doi.org/10.4018/978-1-5225-0489-4.ch012}
}

TY  - CHAP
TI  - Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey
AU  - Biba, Marenglen
AU  - Vajjhala, Narasimha Rao
AU  - Nishani, Lediona
T2  - Collaborative Filtering Using Data Mining and Analysis
PY  - 2017
DA  - 2017
SP  - 217
EP  - 235
PB  - IGI Global
DO  - 10.4018/978-1-5225-0489-4.ch012
UR  - https://www.narasimharao.net/research/visual-data-mining-collaborative-filtering-survey/
LA  - en
ER  - 

Links and files

• DOI: https://doi.org/10.4018/978-1-5225-0489-4.ch012
• This page as text: https://www.narasimharao.net/research/visual-data-mining-collaborative-filtering-survey/paper.md
• Permanent address of this page: https://www.narasimharao.net/research/visual-data-mining-collaborative-filtering-survey/