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Research on Recommender systems

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Jingjing Zhang

on 14 August 2014

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Transcript of Research on Recommender systems

A Research Overview
Recommender Systems:
Thank you!
Our World is an over-crowded place
My Research Topics
Longitudinal Dynamics
Consumer-Recommender Interaction
Performance Evaluation
Performance Evaluation and Improvement
Jingjing Zhang

Assistant Professor
Dept. of Operations and Decision Technologies
Kelley School of Business

We are overloaded
But we really need and consume only a few of them!
Can Google Help?
Can Facebook Help?
Can Experts Help?
Yes, but only when we really know what we are looking for

What if I just want some interesting music tracks?
- Btw, what does it mean by “interesting”?
Yes, I tend to find my friends’ stuffs interesting

What if I had only few friends, and what they like do not always attract me?
Yes, but it won’t scale well
- Everyone receives exactly the same advice!

It is what they like, not me!
- Like movies, what get expert approval does not guarantee attention of the mass
Performance Evaluation
Consumer-Recommender Interactions
Longitudinal System Dynamics
Temporal Dynamics
To understand how user-recommender interactions affect the temporal dynamics of recommender system, products and consumers
e.g., how do users’ consumption patterns influence system accuracy, product sales diversity, and user consumption relevance over time
Impact of Recommendations
Feedback Loop
Anchoring Effects
: Do system recommendations displayed to consumers influence their preferences?

Biased recommendations not only impact consumers' preference ratings, but also their economic behavior
How do we know the recommendation is good?
Mostly measured by
Closeness between predicted rating and actual user rating
e.g., MAE, RMSE, Precision, Recall
Predictive accuracy isn't enough
Other metrics:
coverage, diversity, novelty, robustness, profitability, etc
To recommend something we may like
- It may not be popular
- Based on our history of using services
- Based on other people like us
OK, here is the idea called Recommender Systems !
We need Help!
Define new performance evaluation metrics:
evaluate stability of popular recommendation algorithms in broad way of settings
Improve stability of recommendation algorithms:
meta-algorithm development
Impact of data characteristics on recommendation performance:
rating data characteristics are associated with the variation in recommendation performance
How to correct the anchoring biases of consumers’ preferences caused by interacting with recommender system?

Algorithmic Approach
System Design
Consumer Education

of prediction
predictive ability of a recommender systems

aggregate consumption diversity
of consumed products

Research on Recommender Systems
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