We all use the "wisdom of the crowd", but the crowd, what to do, is made up of lots of individuals who behave differently

Who among us has not had this happen? We order dinner from a highly rated restaurant, and are disappointed to discover that the food is too spicy for our taste, that the portions are too small for us, or that the combination of flavors is too bold for our liking. Was the crowd we trusted wrong? Not really. For other people, this dinner might be perfect - but it's not what we were looking for when we were looking for a recommendation. Prof. Tova Milo from the Blavatnik School of Computer Science at Tel Aviv University, who serves as the Dean of the Faculty of Exact Sciences, researches databases - and in this context, tries to mine information from the crowd.
"I research the field of databases, and in particular the famous 'big data', and the part of it that is hidden in the minds of the masses," says Prof. Milo. "The main goal is to enable the intelligent collection of information from the crowd, in all kinds of fields and for all kinds of needs. But then the question arises: Who is the crowd? Who is relevant to a certain search? An example I like to give: a person is looking for a hotel. If he is a 'spoiled prince' he prefers a hotel Five stars, let the room shine. Another person will settle for a three-star hotel - as long as the hotel is in the city center and a third person is only interested in staying at the same hotel And rate it completely differently. The crowd is too massive. The question is how do we define the relevant crowd - and in the second step, how do we find it in the sea of data available to us." Today, the existing tools for searching for the desired wisdom of the crowd are somewhat limited tools, because the relevant crowd is rigidly defined in them.
"The algorithms on which these tools are based have very specific definitions for questions like 'what is a good crowd' or 'who looks like me,'" explains Prof. Milo. "The fundamental insight of our research is that it is not advisable to use a predefined fixed characterization, but to conduct a task-dependent search. For example, in the hotels example: if for the most part I am looking for people who are similar to me in everything, in this specific case I need people who are similar to me only in a very specific angle - People who enjoy vacations the way I enjoy vacations and are looking for what I'm looking for when I go on vacation. Their opinion is more relevant to my search than the opinions of tons of other users, even if they are They are more similar to me in other aspects such as education, social belonging or residential area."
Using the research grant from the National Science Foundation, Prof. Milo and her colleagues are developing tools that will help users easily define who is the crowd that is relevant to their task.
"Ideally, we want the system to give us convenient tools to define the type of crowd that is relevant to a certain task and to derive the algorithms by itself, that is, the correct and efficient ways, to find that crowd. The ambition is that the system will be able to translate our definitions into an automatic action plan for collecting the necessary information, and that all the hard work will be Hidden from the user's eyes. Basically, the idea is to program a generic system, which will allow any user - private and public - to search the The crowd is relevant for every need: from a consumer recommendation, through the possibility of contracting a disease to an advertising agency that wants to know where it's worth investing the resources. The needs are at least as diverse as the 'crowd'."

Life itself:
"I guess everyone has their own passion," says Prof. Milo, "and my passion is running. I'm addicted to running. I run 10 km every morning."
More of the topic in Hayadan: