Internet Hungary Innovation Competition


Slamby was selected as one of the eight finalists out of the 54 projects in Hungary and we will compete at the finals of the Internet Hungary 2016 Innovation Competition. All finalists have the chance to present their solutions to the professionals on IT field and have their valuable feedbacks.

The competition and the conferences will be held at Siofok, Hungary for two days long from 27 September and the main sponsor for this event is Deutsche Telekom. In recent years, Internet Hungary is one the most important trade forum that promoted in this market segment in Hungary.

We are all excited for the finals, wishing all the finalists good luck and hope to see you there!

Slamby’s Latest Release Extends the Power of the Text – Version 1.0


Slamby v1.0 makes it easy to build insightful services and provides precise solutions for Classifieds

After Slamby launched the Slamby Classifier in 2014, it created a great impact on Classified Market as a first truly language-independent classification engine with a high accuracy level. Since then, Slamby has changed the product development strategy to provide a wide range of solutions dedicated to Classified market instead of providing only classification engine. Today, after numerous preview and beta releases, Slamby is officially launching the first stable release version Slamby v1.0.

Slamby v1.0 introduces a large number of new features via Slamby Server, including open-source user-interface called Slamby TAU and many available Slamby SDKs to work even better by creating custom workflows. The team also moved a lot of the core functionality into UI that now make it easier to work and to increase the productivity from your Slamby Server. Slamby TAU is an integrated data management tool with the ability of quick real-time data access, data-analysis, and data processing.

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Machine Learning and Data Management Solutions Specialized for E-Commerce

Slamby uses Azure’s unique capabilities to deliver award-winning data management services.

Founded in 2013 in Debrecen, Hungary, Slamby-Semantics is an award winning international IT solution firm developing technology that can understand and categorize written language. Specialized for E-commerce solutions, classified ads, and job portal categorization, Slamby’s semantics technology is capable of learning, remembering, and using acquired knowledge to resolve common, every-day tasks – from sorting advertisements, to complex textual analysis, to simple customer service tasks. Slamby’s unique ability to quickly analyze and sort complex text frees people from the tedious process of organizing and categorizing their work, enabling them to be more productive.

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Slamby Similar Product Recommendation Engine (SPR) in 9 Simple Steps

This use case tutorial shows how easy to have high accurate and language independent similar product recommendation engine with Slamby. The tutorial covers the all steps from the beginning till the implementation to your website in Node.JS (JavaScript) language.

1. Collecting your data to work with

At first lets create a dataset on the Slamby Server where we can import our required product data and categories. After it’s done Slamby can use it to learn your classification rules.

During the training process, Slamby will read and understand the text from the interpreted fields in your product database and learn how to categorize your digital products. After the training process, Slamby will be able to recommend similar products with the most relevant products from your product database.

Keep in mind that when the product quantity is higher, accuracy will be higher as well. Especially when there is high amount of category in your database.

Necessary fields inside of category database are ’Category ID’, ’Parent ID’ and ’Category Name’.
Necessary fields inside of product database are ’Category ID’, Product ID’, ’Product Title’ and ’Product Description’.

Example Database


Tips: You can have as much fields as you want in your product database in order to store, monitor, modify and analyze. Mentioned fields are necessary to build recommendation engine.

2. Getting Started

In order to speed up process and avoid complexity, we are going to use Slamby TAU. TAU is a desktop application that makes you able to communicate with your Slamby Server directly to take quick actions. Click here to download the latest release of Slamby TAU.

Slamby TAU Tutorials

For an additional information, you can manage all process by using your command line in several programming languages as well. To know how, visit our GitHub Page to find out our SDKs.

Example TAU Login Screen

Slamby TAU Login Screen

After launching TAU, the first thing to do is to define our server credentials. Click on the New button to create new endpoint. Now, we can set our credentials in a JSON format.

"ApiBaseEndpoint": "",
"ApiSecret": "s3cr3t",
"ParallelLimit": 4,
"BulkSize": 1000

Example TAU New Dataset Screen
Slamby TAU Creating a New Dataset

To create a dataset, first we need to fill a form with the following parameters:

Name: We will define name of our dataset. demo-dataset

NgramCount: This count will determine the maximum n-gram value of our database that will be used during the classification. Default value is "3"

IdField: the ID field name from the sample database. id

TagField: the tag field name from the sample database. category

InterpretedFields: fields from the sample database which contains text to analyze. We want to analyze ad titles and descriptions to get best category recommendation. name, description

SampleDocument: We should define the schema of our product database.

"id": 10,
"name": "thisistheadtitle",
"description": "thisistheaddescription",
"category": [
"price": "$100"

3. Importing Data into the Dataset

Next step is importing our product and category databases into the dataset.

Example TAU Data Import
Slamby TAU Dataset Importing

  1. By right-click on created dataset, we can select to import document from ”csv or json” and import tag from ”csv or json”.

  2. After selecting the right import format, we can select our source file using file browser.

  3. Then, – a setting window pops-up. Here we can set the delimiter that will apply during CSV parsing. There is also a force import checkbox. Using force mode, all the errors will be detected and reported, but the import will be continued anytime. Not using Force mode, import process will stop when the first error detected.

Tips: If you want to fix the errors inside of your database, don’t use force mode. In that time, Slamby will detect the mistakes particularly and you can easily fix those one by one.

4. Creating a Similar Product Recommendation Engine (PRC Service)

Next step is building PRC service using “Slamby Twister” technology.

Example TAU Create New Service Screen
Slamby TAU Create New Service (PRC)

To create a PRC Service, we will provide the required name and the short description of it in order to recognize the service in our future actions. Select Prc as a type of service, and click on the ‘Ok’ button. The service is going to be displayed in ‘New status’.

5. Preparation of SPR Engine

Next step is preparation of ‘PRC Service’ by providing our custom settings as a single JSON.

Example TAU Preparation Setting Screen

Name Description
DataSetName Source Dataset name that we are going to use to create PRC Service. We will use ecommerce_dataset that we have created.
TagIdList Tag IDs that we are going to use for SPR. We will keep it as null, all the Leaf Tag Ids will be used or we can provide the specific Tag IDs to be used for SPR.
"DataSetName": "demo-dataset",
"TagIdList": null

Tips: to select your custom Tag Ids and paste it into the JSON setting, select your required Tags in Data>Tags, and press ctrl+c, then ctrl+v in the json document. The selected Tag IDs array will be pasted as a JSON array.

6. Activation of SPR Engine

Next step is adjusting the activation settings of ‘PRC Service’ in a JSON format.

Example TAU Activation Setting Screen
Slamby TAU PRC Service Activation

To activate the PRC Service, we can define interpreted fields for recommendation. But now, we will keep it as 'Null' to use all the fields.

"FieldsForRecommendation": null

7. Testing of Similar Product Recommendation Engine

Now, our recommendation engine is ready for testing…

Example TAU Recommend Screen
Slamby TAU PRC Recommendation

To test the service, we should fill the JSON setting input form with the available settings and to send our request to the Service API endpoint.

Name Description
Text Here, we can type the text to be analyzed by the PRC Service. This text will be analyzed and service will show us the similar product recommendations. "Limited Edition iPhone 6s 128GB Custom Matte Black Gold Logo & Buttons"
Count We can adjust the counter to determine the quantity of recommended products. We will type 5 to receive 5 most similar similar products as a result.
NeedDocumentInResult We can set the Need document in result as true in order to receive full detailed document response.
TagId We can set the TagID to determine the Target Tag ID where the analysis is going to be processed. We will keep it as null add all the Leaf Tag Ids will be used.
Filter By using Filter, we can make the analysis on the filtered documents. But, we will keep it as null and all the documents will be used for the analysis.
Weights By using Weight, we can customize our fields in an order. But, we will keep it as null and the results will be in an order according to the score.
"Text": "Limited Edition iPhone 6s 128GB Custom Matte Black Gold Logo & Buttons",
"Count": 5,
"NeedDocumentInResult": true,
"TagId": "4",
"Filter": null,
"Weights": null

8. Slamby SDK with Node.JS (JavaScript) Language

Open up your server file with the extension .js by using text editor (such as notepad). Just type out and modify the below code. Then, save it.

var SlambySdk = require('slamby-sdk');

var apiInstance = new SlambySdk.PrcServiceApi();

var id = "bde8cf2e-b543-456f-8d61-70769b923ae8"; // String |

var opts = {
'request': new SlambySdk.PrcRecommendationRequest() // PrcRecommendationRequest |

apiInstance.recommendService(id, opts).then(function(data) {
console.log('API called successfully. Returned data: ' + data);
}, function(error) {

Click here to download the sample server.js file that we have already prepared for you.

Tips: You should modify the below listed items:
+ client.basePath with your ‘Server URL’
+ Authorization with your Server API Secret
+ ID with your PRC Service ID, which is created automatically after you created your service by using TAU.

9. Try it out

Enjoy your demo.