Libraries tagged by recommend

Acquia recommended acsf drush commands.

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Wrapping Redis's sorted set APIs for specializing recommending operations.

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The Recolize Recommendation Engine Extension for Magento 2

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Adds a utility that provides templating performance recommendations.

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Google Places API integration for Contao Recommendation Bundle

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Provides Smart Product recommendations functionality for shoppers

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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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Relewise is a next generation personalization SaaS-platform, which offers functionality within product- and content recommendations and personalized search. This official SDK helps you interact with our API.

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PHP implementation of the (Weighted Slopeone,Cosine, Weighted Cosine) rating-based collaborative filtering schemes.

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A Laravel wrapper for the recommendation system Easyrec

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You may wish to store a larger option value than is recommended on WordPress.com. If your option data will exceed 400K, or is of an unpredictable size (such as an HTML fragment etc.) you should use the wp_large_options plugin to store the option in a cache-safe manner. Failure to do this could result in the option not being cached, and instead fetched repeatedly from the DB, which could cause performance problems.

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The payments API is used to create a session to offer Klarna's payment methods as part of your checkout. As soon as the purchase is completed the order should be read and handled using the [`Order Management API`](https://docs.klarna.com/api/ordermanagement). **Note:** Examples provided in this section includes full payloads, including all supported fields , required and optionals. In order to implement a best in class request we recommend you don't include customer details when initiating a payment session. Refer to [Initiate a payment](https://docs.klarna.com/klarna-payments/integrate-with-klarna-payments/step-1-initiate-a-payment/) section for further details. Read more on [Klarna payments](https://docs.klarna.com/klarna-payments/).

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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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