Recommendation system defines or predicts the preferences or ratings of any product or item related to each person’s choice. These are used in almost every field. Recommend product, movies, product, song and video and books according to each person’s past data of choice. The system analyses past activities from which recommendation can be made easily. However, recommendation-as- a-service platforms started to proliferate in the past couple of years and they transformed the whole recommender system business. Different types of recommendation systems and engines are available in market.
A recommendation system plays an important role in e-commerce. They recommend products based on the need and taste of consumers. Many e-commerce websites gain large profits due to these recommendations. The most commonly used recommender system typically produce a list of recommendations through collaborative or content-based recommender based on previous choice and need of consumer. Our models based on neural network structures make prediction for a particular user based on that user’s previous information and data. Research Infinite solution offers services for machine learning based recommendation system.
Why deep learning based recommender system?
Now, Recommender system has become an essential part in industry area and a critical tool to promote sales and services for online websites and mobile applications. For example, movies watched and many video clicks are due to recommendation. Deep neural network based recommendation algorithm for recommendation have a lot of improvement over traditional models. So deep learning based recommendation methods has increased exponentially in these years.
Most of these systems offers analytics and customizations leverage the solutions to its end. These are very easy to implement and provide reliable results. Many services such as Book recommendation services, news articles recommendation and many more are in trend these days. All this is possible due to 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.
Other Recommendation services are: There are various e-commerce applications of this system. These are: Movie / Video/ Song Recommendation System: A system that helps users to discover items may like is known as recommender system. Let’s elaborate it in a simple way, whenever we try to watch some movies on Netflix, searching clothes online, watching videos on YouTube, you will get message like this
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Activity Recommendation System: The system analyses past events of a person like what a person like what they order, eat or drink, where he visits the most. Further from such information next activities are recommended by this system according to taste and type of person.
Health Recommendation System: This System representing patients' health status learning from previous patient’s database and make digital information available for patient-oriented decision making. It evaluates patient’s health and recommends treatment based on patient’s health record system.
Travelling recommendation system: This system primarily uses a content-based approach, in which the user express’s needs, profits, and limitations using the offered language. The system then contests the user inclinations with items in a catalog of destinations. Thus according his search and most visited destinations, their trips with discount packages are offered.
Moreover Recommendation systems are very powerful for extracting valuable information and generating more sales.
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