Visualization of music suggestions

Promotor: Erik Duval, Katrien Verbert

Supervisor: Katrien Verbert

Overview

I. Background

II. Issues

III. Related work

IV. Objective

V. Prototype

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Overview

I. Background

II. Issues

III. Related work

IV. Objective

V. Prototype

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I. Background

• Recommender system

– Automated suggestion generation

– Strategies:

• Content-based

• Collaborative

• Hybrid

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I. Background

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Content-based Filtering

I. Background

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Content-based Filtering

I. Background

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Content-based Filtering

I. Background

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Collaborative Filtering

I. Background

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Collaborative Filtering

I. Background

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Hybrid Filtering

Overview

I. Background

II. Issues

III. Related work

IV. Objective

V. Prototype

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II. Issues

• Cold start:

– New users

– New items

– Unsufficient data

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II. Issues

• Trust

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II. Issues

• Comparison performance

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II. Issues

• Provide the user with feedback

– Estimated accuracy (confidence)

– Insight into recommendation process

– Steering the recommendation process (control)

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II. Issues ?

• Provide the user with feedback

– Estimated accuracy (confidence)

– Insight into recommendation process

Through visualization

– Steering the recommendation process (control)

Through interaction

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Overview

I. Background

II. Issues

III. Related work

IV. Objective

V. Prototype

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III. Related work

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III. Related work

• Smallworlds

– ACF

– FB API

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III. Related work

• Tasteweights

– Hybrid

– Wiki

– Twitter

– FB

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Overview

I. Background

II. Issues

III. Related work

IV. Objective

V. Prototype

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VI. Objective

• Designing, building and evaluating an explanation system for a certain dataset with a given recommender strategy

– Using visualization

– Using interaction

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VI. Objective

• Focus:

– User insight

– User interaction

– Item discovery

– Visualizations

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VI. Objective

• User insight:

– Into who his/her neighbors are and why they are his neighbors through visualization;

– Into which items are recommended to him/her and why;

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VI. Objective

• User interaction:

– A user must be able to steer the recommendation process through user-interaction;

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VI. Objective

• Item discovery:

– A user is aided in looking for new items through visualization;

– A user is aided in looking for similar users through visualization;

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VI. Objective

• Visualizations:

– Must be elegant and direct;

– Must be a UI element that supports a user in using an application, rather than be an application by itself, therefore an important aspect is one of re-usability and scalability;

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Overview

I. Background

II. Issues

III. Related work

IV. Objective

V. Prototype

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V. Prototype

• Technology:

– Google Chrome extension

– Last.fm API

– JavaScript

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V. Prototype

• Visualization: graph

– Nodes: artists

– Edges: users that have the artists connected by the edge in their profile

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V. Prototype

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V. Prototype

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V. Prototype

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Overview

I. Background

II. Issues

III. Related work

IV. Objective

V. Prototype

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Questions

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