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

Unraveling the True Meaning of “Admission to the Bar” | October 14 2025, 01:20

It turns out that the phrase “barristers must gain admission to the bar” is not at all about bars and baristas, as I would have thought, had I not read that it’s actually about lawyers in the US. Admission to the bar” — is the official admission to legal practice (for barristers). And a Barrister” is a lawyer who represents clients in court. There’s also Solicitor” — a lawyer who works with clients and documents.

Historically, bar” literally means a bar (barrier) in court, separating the area where the judges and lawyers sit from the rest of the hall. Being called to the bar” means being called to the barrier,” i.e., being admitted to represent cases in court. Today, the bar” refers to the legal profession as a whole or the legal community.

Actually, it all started when I saw the title (professional designation) “Esq.” with a guy’s name and realized I didn’t understand any of these letters often listed after names. There are a lot of them, and you’ve probably seen PhD, M.D., or CPA numerous times.

From Vision to Bookshelf: Launching “Recommender Algorithms” | October 13 2025, 11:54

Finally, I have released a book! It is called Recommender Algorithms — it contains more than 50 recommendation algorithms with mathematical explanations, detailed descriptions, and code examples.

It all started early in the spring in Germany, when I attended the ACM conference and made the first sketches of the book’s structure, analyzing reports on the RecSys stream. And now, six months later, the book has been published.

Why did it appear? Because there is no single, accessible source either online or in print where the recommendation algorithms of various types and purposes are thoroughly examined. There are articles focused on narrow aspects, but to collect and systematize the developments — from fundamental to the most recent — until now, it seems, no one has managed to do it for some reason. Maybe no one needed to. Suddenly, I found I needed to. I don’t know if I succeeded, but I am eager for your feedback.

Available on Amazon and Barnes and Noble. There is a Russian automatic translation (surprisingly, but very decent), but I do not know how to sell it yet.

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

(This is not my only book, but today — just about this one.)

Decoding Solr and Lucene: Engineering Insights and Algorithms | October 06 2025, 17:11

Preparing a book for publication on Solr&Lucene. What do you think about publishing such a translation on Amazon? 🙂

The book is about algorithms and under-the-hood engineering. I haven’t seen books from this angle yet, maybe someone will find it interesting.

Exploring the Chaos Game: Creating Fractals From Randomness | October 04 2025, 15:32

I read something interesting today. About fractals. If you take any three points that form a triangle, and then a fourth point anywhere, and subsequently throw a dice, the faces of which are assigned to the first three points. Next, you move from the current point towards the point corresponding to the result on the dice and place a new point halfway; this becomes the new current point. After many iterations, the points start to form the Sierpinski triangle – the one shown in the attached picture. Intuitively, you would think the triangle should be fully filled because it involves random movements in three directions from a randomly chosen point, but no. Moreover, it works even if the starting point is inside the future empty triangle (yes, a few points will disrupt the picture, but that’s it). If you start our experiment with five or six points instead of three, different shapes will form – see the attached picture. This graphical method is called the Chaos Game.

By the way, it may seem obvious, but in case you wondered — all the presented figures have zero area.

If you take two triangles and with a probability p move towards random vertices of the first, and with (1-p) towards random vertices of the second, you end up forming a Barnsley fern (picture №2).

I love such things because they seem like magic at first glance 🙂

(It’s a kind of problem from the same class as the synchronization of metronomes)

Celebrating Marcia Klioze at the Arts Club of Washington | October 03 2025, 22:42

Friends, I am currently at the opening of Marcia Klioze’s exhibit at the Arts Club Of Washington and I am absolutely thrilled! I am so happy for my wonderful mentor, from whom I have been learning oil painting for two and a half years. Today her solo exhibition is here, and the atmosphere is simply magical.

I am proud to be learning from her invaluable experience and learning to see the world anew. Next Tuesday is another class😉 I’ve wanted to post her works for a long time, and today I finally have the opportunity to share (I asked for permission, so it’s all official)!

Almost all works are for sale, for those who are interested, do drop by

Understanding Jerusalem Syndrome and Its Global Counterparts | October 01 2025, 16:10

Listening to Sapolsky in the background, he mentioned Jerusalem Syndrome. It’s when a deeply religious American Baptist from the southern USA, having saved money and prepared, arrives in the Holy Land and sees that Jerusalem is just another city: traffic jams, smog, noise, pickpockets, McDonald’s—everything like that. And then—an interesting feature—in all cases, the person tears up sheets, takes off their clothes, and suddenly finds themselves on the streets of Jerusalem, dressed as if in a toga, begins to preach on the streets, calling for a simpler life and all that.

A psychiatric team arrives, takes the person to the hospital for a few days, everything becomes clear, they send him back home, and he never encounters this syndrome again.

Each year in Jerusalem, about up to many dozens of cases are recorded. It’s a recognized syndrome, about which scientific articles are published.

Sapolsky says that if hotels in Jerusalem always had, for example, checkered sheets instead of white ones, which seem to “invite” one to don a toga, it would help prevent the crisis.

But amusingly, there’s a twin brother of this disorder, the Paris Syndrome, which for some reason mainly affects the Japanese. Japanese tourists come to Paris because they are attracted by the culture, language, literature, and history of France, as well as the landmarks of Paris. However, once there, they encounter difficulties such as a language barrier (surprise surprise!), differences in mentality, and disappointment from the reality of Paris not meeting their expectations.

There’s also a milder version called the “Florentine Syndrome.” This often happens during a visit to one of the 50 museums in Florence, the cradle of the Renaissance. Suddenly, a visitor is overwhelmed by the depth of feeling the artist has imbued in the artwork. At this point, they acutely perceive all emotions, as if transported into the space of the image. Victims’ reactions vary up to hysteria or attempts to destroy the painting. Despite the syndrome’s relative rarity, guards in Florentine museums are specially trained on how to deal with its victims.

Overall, be careful with syndromes when you’re traveling.

PS. This image was made for me by google. In the second image, a guy in a tie tells a tearful girl 脆培, which seems just a meaningless set of characters, something like fragile culture. But when I asked ChatGPT, it told me it resembles 脱げ (nugu) — undress 🙂 if you ask Google Gemini to redo it, Google gives the same picture, where he’s also shouting 暁は, but at the same time, he has already taken off his shirt. But that’s also unclear what 暁 – it’s dawn. Generally, with Japanese, LLM is bad. I’ll leave the second image in the comments. By the way, there are several differences there, you can play a game to find ten differences. They are amusing

Introducing the AI-Powered Text-to-Diagram Generator | September 30 2025, 20:57

While working on a book, I realized what kind of product I’m missing. It’s an AI diagram generator based on textual descriptions.

The idea is that the master document for the diagram is text. This textual description can be (and should be) quite detailed, so the generated diagram exactly matches the author’s vision. The diagram itself is not edited. That is, it can be edited – moving circles around, but ideally, after making changes, the system should update the text, generating from which will result in what the user adjusted.

The result — the diagram — should correspond as closely as possible to the description. If it does not match the description because, for example, it’s impossible to make a triangle with three obtuse angles, the system should do its best and provide a verbal response about what didn’t work. The user can then modify the task so that the system complies and produces the diagram correctly.

But then we understand that the author might have randomly achieved something that they liked with their flawed text. And if regenerated, it might turn out differently, and not necessarily better. Therefore —

You could ask the system to generate a diagram description from the diagram, which, if inputted back into the diagram generator, would result exactly in what the description was generated from. Yes, this description would be more verbose and complex, but it would more reliably describe the result.

So, from this point, you are no longer working with the diagram. You are working with text. If a diagram is needed — you simply compile the text into a diagram and it turns out as needed. But you don’t even work directly with the text. You work with this diagram-description text through an LLM, asking it to add some block, and the text changes, but changes in a way that everything doesn’t suddenly shift.

The final diagram should be in an object form, from which raster (PNG) or vector (SVG, EPS) images can be created.

It would also be great if such a system could take existing diagrams or diagram templates so that it could borrow styles and existing conventions on how to display what.

So, these are my fantasies. If anyone has ideas on how to implement this — let’s discuss 🙂

Crafting the Future of Recommender Systems: A Deep Dive into Algorithms and Implementation | September 26 2025, 21:17

I decided a while ago to write a book on recommendation algorithms. With mathematics, code examples, a repository, etc. English, of course.

Accordingly, I am looking for volunteer reviewers who are knowledgeable in the field. Also those who have experience with print-on-demand on Amazon.

There’s already about 200 pages of content. About three months of work left. Working title Recommender Algorithms in 2026: A Practitioner’s Guide. Roughly half of it is still in draft form, with the first 80 pages about 80% complete.

I’ve built a mechanism to publish in HTML and PDF simultaneously. The HTML version is fully functional, with navigation. The navigation block reflects the current section, and as you scroll, it shifts to the one in front of the reader. Clicking on a section, of course, teleports you to what you clicked on. It’s all completely automatic.