Understanding Biases in Search & Recommender Systems via @gregjarboe
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Understanding Biases in Search & Recommender Systems
One of the most thought-provoking presentations at TechSEO Boost was the keynote by Dr. Ricardo Baeza-Yates, the CTO of NTENT. It was entitled, "Biases in Search and Recommender Systems."
Spoiler alert: Bias is bad; delivering and monetizing the most relevant content based on user intent is good.
Now, prior to joining NTENT in June 2016, Dr. Baeza-Yates spent 10 years at Yahoo Labs as Vice President of Research, ultimately rising to Chief Research Scientist.
He is an ACM and IEEE Fellow with over 500 publications, tens of thousands of citations, multiple awards and several patents.
He has also co-authored several books including "Modern Information Retrieval", the most widely used textbook on search.
So, his presentation wasn't insubstantial, unsupported, or sensational arm-waving. It was a careful examination by an expert in the field of most of the biases that affect search and recommender systems.
This includes biases in the data, the algorithms, and user interaction – with a focus on the ones related to relevance feedback loops (e.g., ranking).
And instead of accusing Google, YouTube, and Amazon of being biased and urging fair, impartial, and unbiased politicians to take drastic action, Dr. Baeza-Yates methodically covered the known techniques to ameliorate most biases – including ones in site search and recommender systems that can cost ecommerce businesses some serious money.
What Is Bias?
Dr. Baeza-Yates started by defining three different types of bias:
Now, most critics of search and recommender systems focus on cultural biases, including: gender, racial, sexual, age, religious, social, linguistic, geographic, political, educational, economic, and technological.
But, many people extrapolate results of a sam