Z5 offers AI Powered Recommendation as a Service to increase the sales and growth of E-commerce Industry. Why you are sharing your Customer’s Confidential data to other Product Recommendation Engine? Create your Own highly Customize AI-driven ecommerce recommendation engine tailored to each Customer online and in the store.
Recommendation Engine algorithms has an ability to customized the content, based upon the past behaviour, which brings customer delight and give them a reason to keep returning to the website. Recommendation Engine algorithms defines or predicts the preferences or ratings of any product or items related to each person’s choice. These are used in almost every field for Product Recommendation Engine, e-commerce recommendation, music recommendation engine and videos and books recommendation according to buyer’s past data of choice. The system analyses past activities from which recommendation can be made easily.
Content Based Filtering Systems: Content based Product Recommendation Engine generates recommendations based on items and attributes and their similarities. Item refers to content whose attributes are used in Recommendation models. These could be books, movies, documents etc. Attributes means to the characteristics of an item. A movie tag, words in documents are example. This kind of AI Powered Recommendation as a Services deliver using machine learning techniques and Natural processing language modelling.
Collaborative filtering System : Collaborative filtering based Product Recommendation Engine generates recommendations based upon on crowd-sourced input. This strategy recommend user’s behaviour and similarly between users. These systems memorize the training data which deploy cosine similarity calculations, correlation analysis and k-nearest neighbour classification. This kind of AI Powered Recommendation as a Services also deliver using machine learning techniques and Natural processing language modelling.
Hybrid Recommender Systems : Hybrid Product Recommendation Engine system is combination of content-based and collaborative approaches. They help us for improving recommendations that are derived from sparse data set. (Netflix is one the example of hybrid Product Recommendation Engine System.
Activity Recommendation System: This AI Powered Recommendation as a Service is responsible to analyse past activities of a person like what a person orders mostly to eat or drink, types of places one visits mostly. Further from such information next activities are recommended by this system according to taste and type of person.
Product Recommendation Engine: This AI Powered Recommendation as a Service prefers a new product to any customer based on their previous search. It extracts the required information from customers previous activities or choices from the database. Like, if there is book recommendation engine, Customer will get some similar books in the recommendations platform. This technique has become very beneficial to sales and marketing field. Real time E-commerce recommendation engine are generated dynamically on e-commerce sites based on purchase habits of a particular person.
Movie/Video AI Powered Recommendation as a Service: Any person who usually watch online movies, or video are preferred with similar items. This is due to recommendation system. Some people also use personal movie recommendation system to check what’s the next similar item. This kind of recommendation system analyze the behaviour of song like Jazz, Bass, Pop according the previous song list of user. We are providing videos and songs Music recommendation engine services, so people can listen their favourites according to choice.
Human Health Diagnostic Medical Systems: This kind of AI Powered Recommendation as a Service includes the human health diagnostic, where machine can diagnosis health symptoms corresponding to the filling user’s information and in few minutes. It will display the diseases with recommended doctors, Excecises and meditation. User can get their daily or Weekly report with health improvemnet chart and Consult to the Doctor anytime via Video call, Chat and Physically Visit. We deliver Complete Medical Business Intelligence application which data visualization, data analytics and patient monitoring system, patient insights for tracking the Medical Record.
Customer Services Recommendation Engine: This kind of AI Powered Recommendation as a Service based on customer segmentation, which is a good way to overcome the problem of collaborative filtering algorithm. Engine will analyze the behaviour of customer data reviews or comments and recommend the products according to the previous purchase history. Our recommender systems services are based on customer segmentation, this segmentation makes effective allocation of marketing resources. It is very useful in e-commerce websites.
Music Recommendation Engine: Using machine learning algorithms we build Product Recommendation Engine which can automatically predict genre of any song along with its instrumentation (type). Any song can be given as an input and the system will provide whole information about that music. Also similar songs can be recommended through this system.
Automatic Tagging and Recommending: Tagging on Social media websites has become so popular. It means connecting any song, video or person within a particular stuff. But our popular AI Powered Recommendation as a Service approaches provide tag recommendations related to a user-defined-similar keywords. It helps a lot in better management and sharing collections. These e-commerce recommendation engine offers analytics and leverage customizations at the solutions to its end. Our product recommendation engine services help your business to increase more clients and conversion of sales. These product recommendation engine services are easy to implement and provide reliable results. Many services such as Book recommendation services, news articles recommendation engine, music recommendation engine, content recommendation engine and many real time recommendation engines are in trend these days. All these are possible with the implementations of machine learning algorithms. Recommendations are performed by classifying a document into one or more topic clusters or classes and then selecting the most relevant tags from those clusters or classes as machine-recommended tags. Moreover Recommendation systems are very powerful for extracting valuable information and generating more sales.
We are serving AI Powered Recommendation as a Services to Different Industries Like Healthcare, E-commerce, Movie Websites, Music Website, Sports Gaming like NBA, NHL, MLB, Tennis, Restaurant, Hotels, IT Industry and Many more.
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