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Webtunix 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.
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. For example, if you are browsing brown winter jackets, this algorithm will suggest other jackets sharing the same properties (e.g. category: winter jacket, color: brown). The advanced technology of Natural Language Processing can be used to recommend products sharing similarities in description.
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. 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 creates such inter-relationship between product and customers. Contact Webtunix for AI power recommendation as a service. We help to determine which recommendation engine is the best for your business.
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. A recommender system tells that uses the switching hybrid method, and combines two methods of Collaborative Filtering and Context-aware for discovery and selection of service. This algorithm has a rather high performance as well as it overcomes the problem of grey sheep, new consumer, and new service entrance.