Webtunix offers AI Powered Recommendation as a Service to increase the sales and growth of E-commerce Industry. Why is there a need to share the Customer’s Confidential data to other Product Recommendation Engine, when you can create your own highly customized AI-driven ecommerce recommendation engine tailored to each Customer online and in the store. We create smart AI recommendation systems to perfectly meet all your requirements.
Recommendation Engine algorithms have an ability to customize the content, based upon the past behaviour, which enhances the customer delight and give them a reason to keep returning to the website. Recommendation Engine algorithms define or predict 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, videos and books recommendation according to buyer’s past data of choice. The system analyses past activities from which recommendation can be calculated easily.
Our AI Powered Recommendation as a Service is built under three algorithms based upon Machine learning and Natural Language Processing :-
Content Based Filtering Systems: Content based Product Recommendation Engine generates recommendations based on items and attributes. Item refers to content whose attributes are used in Recommendation models. These could be books, movies, documents etc. Attributes stand for the characteristics of an item. Examples include a movie tag, words in documents. For example, if you are browsing brown winter jackets, this algorithm will suggest other jackets sharing the same properties. The advance technology of Natural Language Processing can be used to recommend products that have similarities in description.
Collaborative filtering System: Collaborative filtering based Product Recommendation Engine generates recommendations based on crowd-sourced input. These systems memorize the training data which deploy cosine similarity calculations, correlation analysis and k-nearest neighbour classification. For example, if user A viewed items 1, 2, 3 and user B viewed items 1,2, this model will recommend item 3 to user B. We build collaborative filtering systems which create a great inter-relationship between product and customers. We help to determine which recommendation engine is best for your business.
Hybrid Recommender Systems: Hybrid Product Recommendation Engine system is the combination of content-based and collaborative approaches. They help us to improve recommendations that are derived from sparse dataset. Netflix is one of the example of hybrid Product Recommendation Engine System. A recommender system uses the switching hybrid method, and combines two methods of Collaborative Filtering and Context-aware for discovery and selection of service. The performance of this algorithm is very high and it also overcomes the problem of grey sheep, new consumer, and new service entrance.
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, with such information, next activities are recommended by the system according to the taste and type of person. For example, if a user is searching for a tab, our activity recomendation system will show the results according to the most frequent history searches. This will improve the user experience to a great extent. The businesses that are using activity recommendation engine have seen a high raise in their sales figures.
Webtunix AI has designed activity recommendation systems for online stores, and E-commerce for global clients.
Product recommendation engine is a method of providing personalized service to the buyers. A good product recommendation system allows the marketers to analyze the customer data and then use this data to create individual client profile. This AI Powered Recommendation as a Service shows a new product to any customer based on their previous searches. It extracts the required information from customer’s previous activities or choices from the database. For example, our product recommendation will show the most similar products based on the image or text search. 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 the purchase habits of a particular person.
Any person who usually watch online movies, or videos get results with similar items. This is due to the presence of 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 songs like Jazz, Bass, Pop according to the previous song list of user. We provide videos and songs Music recommendation engine services, so people can listen to their favourites according to their most preferred choices. Our movies recommendation system can recommend the movies or videos based on the genre.
This kind of AI Powered Recommendation as a Service includes the human health diagnostic, where machine can diagnose health symptoms corresponding to the user’s information within few minutes. It will display the diseases with recommended doctors, excercises and meditation. User can get their daily or weekly report with health improvemnet chart and can consult the Doctor anytime via Video call, Chat and Physical Visit. We deliver complete Medical Business Intelligence application with data visualization, data analytics and patient monitoring system, patient insights for tracking the Medical Record.
This kind of AI Powered Recommendation as a Service is 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, that makes effective allocation of marketing resources. It is very useful in e-commerce websites. Customer service recommendation engine will recommend the product based on your previous purchase.
Tagging on Social media websites has become so popular. Tagging means connecting any song, video or person with 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 as the solutions. Our automatic tagging and recommendation system can detect the apparels and accessories worn by models in various images. This improves the user experience to a great extent. Moreover, user no longer have to browse for hours searching for the right product.
By 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. Our music recommendation system is trained on million of images and genre.We then create a song recommender by splitting our dataset into training and testing data. Our music based recommendation engine serves the relevant songs and audio for the audience. Our recommendation engine can recommend the songs based on artists, languages and country as well.
Our product recommendation engine services help your business to increase more clients and result in 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 Service 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.
Better data is the key for the better products. We train you data for Machine Learning and better business analytics. We can annotate, collect, evaluate and translate any type of data in any language.