Libraries tagged by recommender

This a PHP library based on JasperReports library that let you create reports with a graphical tool (We recommend to use iReport).

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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econda Magento 2 extension including analytics, recommendations and personalization

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Recommendations for PHP using collaborative filtering

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## Introduction Send SMS messages using Unifonic Messaging API. Get your dedicated Universal number, and start sending messages today. Unifonic NextGen Restful and HTTP **API's** uses The basic Authentication protocol. All request and response bodies are formatted in JSON. ## Get an account To start using the API you need to send an email to Unifonic to create Appsid for you. ## Base URL All URLs referenced in the documentation have the following base: **basic.unifonic.com** ## Security To ensure privacy we recommend you to use HTTPS for all Unifonic API requests. you can download our HTTPS certificate. [Download] (https://api.unifonic.com/udm/https.zip) ## Formats Unifonic API only supports JSON format. All request must use the Content-type header set to application/json. ## Support We’re here to help! Get in touch with support at and we’ll get back to you as soon as we can or you can contact us throw live chat on our [website] (www.unifonic.com).

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Implementation of jsend recommendations for http responses

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This Composable Commerce PHP SDK is deprecated effective 1st September 2022. We recommend you to use our new SDK here https://docs.commercetools.com/sdk/php-sdk#php-sdk-v2.

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Recommendations plugin for Sylius.

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Increase your revenue, drive conversions and transform your eCommerce site today with Recommend

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Microservice for products recommendation

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This package provides AI generated recommendation tags for articles and stores them

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Recommendation integration for Contao Theme-Manager

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Symfony2 bundle that implements a Recommendations Engine with MongoDB

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Product recommendations and suggestions. People who viewed this item also viewed. People with similar interests also viewed.

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World class recommendation engine brought to Laravel

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