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Effect of randomness on search results – Artificial Intelligence

I am currently working on a search ranking algorithm which will be applied to elastic search queries (domain: e-commerce). It assigns scores on several entities returned and finally sorts them based on the score assigned.

My question is: Has anyone ever tried to introduce a certain level of randomness to any search algorithm and has experienced a positive effect of it. I am thinking that it might be useful to reduce bias and promote the lower ranking items to give them a chance to be seen easier and get popular if they deserve it. I know that some machine learning algorithms are introducing some randomization to reduce the bias so I thought it might be applied to search as well.

Closest I can get here is this but not exactly what I am hoping to get answers for:
Randomness in Artificial Intelligence & Machine Learning

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  1. 0

    I don’t see this mentioned in your post… Elasticsearch offers a random scoring feature: https://www.elastic.co/guide/en/elasticsearch/guide/master/random-scoring.html

    As the owner of the website, you want to give your advertisers as much exposure as possible. With the current query, results with the same _score would be returned in the same order every time. It would be good to introduce some randomness here, to ensure that all documents in a single score level get a similar amount of exposure.

    We want every user to see a different random order, but we want the same user to see the same order when clicking on page 2, 3, and so forth. This is what is meant by consistently random.

    The random_score function, which outputs a number between 0 and 1, will produce consistently random results when it is provided with the same seed value, such as a user’s session ID

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  2. 0

    Your intuition is right – randomization can help surface results that get a lower than deserved score due to uncertainty in the estimation. Empirically, Google search ads seemed to have sometimes been randomized, and e.g. this paper is hinting at it (see Section 6).

    This problem describes an instance of a class of problems called Explore/Exploit algorithms, or Multi-Armed Bandit problems; see e.g. http://en.wikipedia.org/wiki/Multi-armed_bandit. There is a large body of mathematical theory and algorithmic approaches. A general idea is to not always order by expected, “best” utility, but by an optimistic estimate that takes the degree of uncertainty into account. A readable, motivating blog post can be found here.

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