The recommandation system
On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems. The project is very convenient for the users because it helps them to find directly the news or informations they are looking for. The recommandation system is for everyone (every internet user).
How does the recommandation system work ?
A recommendation engine is an information filtering system uploading information tailored to users' interests, preferences, or behavioral history on an item. It is able to predict a specific user's preference on an item based on their profile.
With the use of product recommendation systems, the customers are able to find the items they are looking for easily and quickly. A few recommendation systems have been developed so far to find products the user has watched, bought or somehow interacted with in the past.
The recommendation engine is a splendid marketing tool especially for e-commerce and is also useful for increasing profits, sales and revenues in general. That's why personalized product recommendations are so widely used in the retail industry, eleven more highlighting the importance of recommendation engines in the e-commerce industry.
For a recommendation system to be useful, it should be flexible to new user behavior. It should be able to act in a dynamic environment, providing the users timely information about special offers, changes in the assortments and prices.
Shopping has been, is and will continue to be a necessity for humanity. It's not a long time since we asked our friends for a recommendation for buying this or that product. Hence, it's the essence of human beings to buy items recommended by our friends, whom we trust more. The digital age has taken into consideration this ancient habit. Therefore, any online shop you visit today, you may see some recommendation engine used.
With the usage of algorithms and data, recommendation engines filter and recommend the most relevant products to a specific user. As they say, it's like an automated shop assistant. When asking for something, he also suggests another one that you may be interested in.
Developing product recommendation algorithm models is a research area that grows hour by hour.
How about Machine Learning in recommandation system
In order to provide customers with service or product recommendations, recommendation engines use algorithms. Lately, these engines have started using machine learning algorithms making the predicting process of items more accurate. Based on the data received from recommendation systems, the algorithms change.
Machine learning algorithms for recommendation systems are generally divided into two categories; collaborative and content-based filtering. However, modern recommendation systems combine both of them.
Content-based filtering considers the similarity of product attributes and collaborative methods count similarity from customers' interactions.
Generally, the core of machine learning is to develop a function predicting the utility of items to one another.
With so much information on the Internet and so many people out there using it, it has become of vital importance for organizations to search and provide date to their customers corresponding to their needs and tastes.