At the Sixth International Conference on Learning Representations, Jannis Bulian and Neil Houlsby, researchers at Google AI, presented a paper that shed light on new methods they're testing to improve search results. While publishing a paper certainly doesn't mean the methods are being used, or even will be, it likely increases the odds when the results are highly successful. And when those methods also combine with other actions Google is taking, one can be almost certain. I believe this is happening, and the changes are significant for search engine optimization specialists (SEOs) and content creators. Let's start with the basics and look topically at what's being discussed.
As a doctoral student in Social Work, I find the series, Pocket Guides to Social Work Research Methods", all available from Amazon, to be an excellent collection of social work research texts. I recently finished two works in the series - "Multiple Regression with Discrete Dependent Variables" by John Orme and Terri Combs-Orme, and "Clinical Data Mining, Integrating Research and Practice" by Irwin Epstein. I wish to comment on the latter. I am not only a member of one of the intended audiences for this work - doctoral students in general - but also part of a subgroup within that audience - seasoned practitioners, conducting practice-based, dissertation research utilizing clinical data mining (CDM). This work is especially relevant for such a subgroup as the more experienced practitioner is not only more likely to have extensive knowledge of where available program data resides but also the "practice wisdom" for generating researchable ideas about what might be done to "mine" such data beyond its intended purposes.
It would be easier to count all the stars in the night sky than the number of articles written about the death of SEO. I've never written one personally but I was having a discussion with the author of a great piece here on Search Engine Journal on AI and its impact on search and the question came up: Between machine learning and the limited space available for organic search, is it on its death spiral? The most interesting thing about this question may not be the answer but the journey in understanding the question itself, as it's therein that we understand the strategies that will make it either true or false. Between machine learning, the limited space available for organic search, and the growth of both voice search and personal assistants, is it on its death spiral? To explore this question, we're going to look at each of these three areas individually, what they mean together, and finally (and what you likely most want to know), what you need to do about it.
The research surrounding methods of information retrieval is an entire field of science whose specialists aim to provide us with even better search results – a necessity as the amount of data constantly keeps growing. To succeed in their quest, researchers are focusing on the interaction between humans and computers, connecting methods of machine learning to this interaction. One of these researchers is Dorota Głowacka, who assumed an assistant professorship in machine learning and data science at the Helsinki Centre for Data Science HiDATA at the beginning of 2019. Głowacka is studying what people search for and how they interact with search engines, with a particular focus on exploratory search. This is a search method that helps find matters relevant to the person looking for information, even if they are not entirely certain about what they are looking for to begin with.