Tag: Learning

Rediscovering the 1986 “Chemical Trainer”: A Pioneer in Interactive Learning | November 23 2025, 15:55

At my home in Kolomna, I have a book called “Chemical Trainer” from 1986. I have never seen anything like it before or since.

The material of each of the 54 programs is divided into many small, very short sections, or categories. At the end of each category, one or more questions are posed. This is done to check whether the content of the category is truly understood. For each answer, there is a place in the book to jump to in order to see if the answer is correct. If the answer is wrong, it describes why and asks a new question. If correct — you move further in this quest.

These Germans in 1986 created an interactive textbook even before it became fashionable.

Exploring Recommender Algorithms Through Interactive Visualizations and Sandbox Simulations | November 11 2025, 05:23

I’ve launched an electronic open source application for my book Recommender Algorithms! It’s a “sandbox” where you can “run” various recommendation algorithms with different settings, and view specific visualizations for each algorithm that help understand how it works. For instance, for algorithms like ItemKNN, SLIM, or EASE, a key visualization is a heatmap of the learned similarity matrix (item-item similarity matrix). This allows you to see which pairs of items the model considers “similar” (or “influencing” each other). For SLIM, for example, a useful “Sparsity Plot” shows that the similarity matrix indeed turned out to be sparse. For associative rule algorithms (Apriori, FP-Growth, Eclat) the visualization is not a graph, but interactive tables with found “Frequent Itemsets” and generated “Association Rules,” which can be filtered and sorted.

Additionally, there is a parametric mechanism for creating a “game dataset” — Dataset Wizard. It works like this – there are template datasets that describe items through characteristics. For example, recipes through flavors. Or movies through genres. The system generates random users with a random set of characteristics from the same set — and there are many sliders to make this distribution more contrasted or complex. Next, a matrix of user ratings of items is created – conditionally, if the characteristics of the user and the item match, then the rating will be higher because “tastes match”; conversely, if they differ, then the rating will be lower. Here too, sliders add noise and scarcity – randomly removing part of the matrix. The characteristics of products and users are not fed into the recommendation algorithm; they are hidden, but they are used to visualize the results.

The third component of the application is the tuning of hyperparameters. Essentially, it’s an auto-configurator for a specific dataset. An iterative approach is used, which is much more efficient than a full search (Grid Search) or random search (Random Search). In short, the system analyzes the history of past runs (trials) and builds a probability “map” (surrogate model) of which parameters will likely yield the best result. Then, it uses this map to smartly choose the next combination to test. This method is called Sequential Model-Based Optimization (SMBO).

The code is open source and will be further supplemented with new algorithms and new visualizations.

Link to the code in the comments.

Link to the site where the code is deployed and where you can check out the application is also in the comments.

Unveiling “Recommender Algorithms”: A Comprehensive Guide on Recommendation Systems | October 25 2025, 17:36

I finally released a book on #RecSys! It’s called Recommender Algorithms, where I’ve compiled over 50 recommendation algorithms with detailed mathematical derivations, thorough explanations, and code examples.

https://www.testmysearch.com/books/recommender-algorithms.html

It all started early this spring in Germany, when I attended an ACM conference and sketched out the first structure of the book while analyzing the talks from the RecSys track. And now, just six months later, it has come to life.

Why did I write it? Because neither online nor in print is there a single, accessible resource that deeply explores recommendation algorithms of various types and purposes. There are articles focused on small subsets, but collecting and systematizing approaches—from foundational methods to the very latest—seems to have never been done before. I don’t know if I succeeded, but I’d love to hear your feedback.

Please like & share!

P.S. Click at READ SAMPLE to see the first 40 pages. The table of contents is there as well.

https://www.testmysearch.com/books/recommender-algorithms.html

https://www.testmysearch.com/books/recommender-algorithms.html

Childhood Curiosity and the Mysteries of Soviet Electrical Engineering | September 23 2025, 17:00

I remember being puzzled as a child by who the idiot was that decided to make the radio plug exactly the same as the one for 220 volts. This radio plug was supposed to go into a radio socket.

As a child, I used to disassemble and “improve” almost everything electrical in the house (I hadn’t graduated to electronics yet). Of course, I got shocked many times by the outlet, but to my surprise, the old Soviet phone could also give a shock. When a call came in, the voltage in the line would jump from 12-60 volts to 120 volts 🙂

I also had an interesting experience with Christmas lights. For a younger schoolchild, it was unclear why Christmas lights could shock you since they used the same bulbs that I connected to a flat “Planeta” battery. I had to learn the technicalities 🙂 By the way, those square flat batteries have disappeared; they used to be everywhere.

Misguided Lessons with Grok: A Bilingual Blunder | August 19 2025, 23:43

Today Grok blew my mind. I say, teach me French. He says, ok, how do you say “book”? I say “le livre”. He says “wrong! la livra”. 😳The car drives itself anyway, decided to record the dialogue. He’s not convinced. At all, insists on his point. La livra and that’s it. I’m afraid Grok will teach the bad stuff in his Language Tutor mode.

I remembered a story from “Memoirs of Pushkin” by M. E. Yuzefovich, dating to 1829:

he had several books with him, including Shakespeare. One day in our tent, he translated some scenes to me and my brother. I had once studied English, but having not fully learned it, I subsequently forgot it. However, I still recognized its sounds. In Pushkin’s reading, the English pronunciation was so distorted that I suspected his knowledge and decided to test it. The next day, I invited his relative, Zakhar Chernyshev, who knew English as his native language, warned him what was going on, and called over Pushkin with Shakespeare. He willingly started translating for us. Chernyshev burst into laughter at the first words read by Pushkin: “First tell me, in which language are you reading?” Pushkin laughed in turn, explaining that he had taught himself English, and therefore he reads English letters like Latin ones. But the fact is that Chernyshev found the translation completely correct and the language understanding impeccable.”

Anna Derevenitskaya

The Crucial Difference Between “Honoree” and “Gonorrhea” | August 13 2025, 00:46

There is a big difference between “honoree is coming” and “gonorrhea is coming”

The main thing is not to confuse them

From Forbidden Fruit to Linguistic Roots: The Curious Case of Currants and Smorodina | July 17 2025, 13:09

You know, 99.9% of Americans have never tried blackcurrant. It was legally banned here in 1911 because blackcurrants carried a disease that killed pine trees. And along with it, gooseberries and Kinder Surprise were banned too. It even got to the point where in the USA, purple Skittles are grape-flavored, while in Europe, they taste of blackcurrant.

But today I am thinking about something else. I wondered why in Russian blackcurrant is called ‘smorodina,’ and in English, it’s called ‘currant.’ It turns out that ‘smorodina’ is related to the word ‘smrad,’ which meant a strong smell because, according to our ancestors, it smelled bad. ‘Smrad’ used to mean any strong smell. I don’t know how unpleasant it was for them, but this differentiated it from gooseberries, both of which grew along rivers, hence in Ukrainian and Polish, it’s also called ‘porzeczka’ and ‘porichka,’ especially the red and white varieties. To me, gooseberries even smell stronger.

The English name is also interesting. The English ‘currant’ stems from the Middle English ‘rayson of Corantes’ (‘grapes from Corinth’), where ‘Corantes’ is a distortion of the Greek city Corinth. In the Middle Ages, small dried grapes were actively imported into England from Greece (specifically the region around Corinth) and these dried berries were called ‘raisins of Corinth,’ which later shortened to ‘currant.’ Originally, ‘currant’ referred specifically to raisins, dried grapes (essentially, small raisins). And it still means that in some places.

But then a shift in meaning occurred. Later, when shrubs of the Ribes genus (currant bushes), specifically Ribes rubrum (red currant) and Ribes nigrum (black currant), began to be cultivated in Northern Europe, they were given the same name, since their berries were also small and dark like the Greek raisins. Thus, the word ‘currant’ came to be used to denote both currants and gooseberries 🙂 but later on they were differentiated. Yes, gooseberries and currants turned out to be related both biologically and etymologically.

And do you remember the fairy tale about the good heroes and warriors Dobrynya Nikitich, who fought the three-headed Chudo-Yudo on the Kalinov Bridge spanning the River Smorodina? Well, that river, Smorodina, marked the boundary between the world of the living (Yav) and the world of the dead (Nav).

AI-Powered Smart Glasses: Revolutionizing Real-Time Discussion and Information Access | July 15 2025, 20:19

Here’s what would be great to do with AI – a system that reads the screen, listens to what’s being discussed on the call, including what you say, and what is said to you, and _on the screen_, and better yet, directly on smart-glasses, gives pop-up tips and hints that help you timely ask a counter-question or request a clarification, or respond to a question directed at you. Not just for passing interviews, although that would also be nice, but for more effectively conducting discussions — from technical to commercial ones.

In the case of smart-glasses, you could enjoy this without a computer in front of your eyes. I’m just afraid of having to send absolutely everything that happens around you to the cloud, analyze it, and return it in real time, which is technologically challenging (=expensive).

Such a system would be no less useful for conducting interviews than for passing them. For example, you ask someone a question, they start to respond, and then the system suggests — aha, it seems they are struggling with this topic. Let’s ask this question. Then you decide whether to ask this or something else. Why not? It’s convenient. Of course, the interviewee could employ the same system, and then it would not be simple.

Right now, I’m flipping through a book by Johannes Itten on color, and I think about how I miss dynamic illustrations and commentary. I’ve reached Piero della Francesca and for the life of me, I can’t recall what his paintings are like. This is where smart-glasses would come in handy. You look at a word, snap your fingers, and around it appear pop-up windows with contextual illustrations, comments, and links to detailed information, which you can visit now, or save to read later. It would be possible to ask any question verbally while looking at the text segment it pertains to and get an answer not verbally, but in a pop-up window that you can quickly close if you didn’t find anything new, or perhaps add a clarification by voice, after which the content in the window updates.

If I had smart-glasses, I would experiment with this. It seems straightforward.

Advancing Full-Text Search: Testing and Refining with Multi-User Platforms | July 06 2025, 04:35

I have developed expertise in full-text search testing. Essentially, it’s a turnkey multi-user platform that, given roughly 1000 queries and several search engine configurations, can produce reports with graphs, metrics, and conclusions by morning, showing why configuration A performs better than B, and here’s why. It calculates all those NDCG@k, MAP, precision, recall, and about a dozen other metrics. It uses LLM, but only at the final stage, after all the math is done.

So, here’s my question. I’m looking for someone who has faced the same issue in their project, to understand the demand and the ask.

The problem the system solves is defined as follows: there is a functional search for goods, documents – Solr, Coveo, Elasticsearch, Algolia – it doesn’t matter, and there are hypotheses on how to improve it, but there is also the fear that improving one aspect might break another. Well, my thing helps to see this in numbers and graphs, providing a conclusion with justification, including statistical significance and other metrics.

It also acts as a virtual search assessor. For each search result, it can give a rating, assessing how well each document matches the query. This is a very non-trivial task (especially for large documents), involving chunking, embeddings, LLM evaluation of relevant chunks, etc. Non-trivial, but it works.

It also can analyze search queries and break them into groups based on similarity. For instance, such segmentation might show that users sometimes separate the words forming a brand name with a space, and sometimes not. These different variants would be grouped together.

I would like to discuss this with someone who knows more about this topic than I do, someone who has/had such problems and has somehow solved them.

I currently feel like my product is unique in the market. Actually, it’s not even on the market yet. But I really don’t see anything similar out there. Maybe nobody needs it?

I won’t publically post screenshots yet. The picture is merely for attracting attention.

Please share if there might be relevant people in your network.