Connecting Social Media to E-Commerce Product Recommendation using Top-K

Now a days online shopping has achieved a tremendous popularity within very less amount of time. Recently few ecommerce websites has been developed their functionalities to a extent such that they recommend the product for their users referring to the connectivity of the users to the social media and provide direct login from such social media such as facebook, Twitter, whatsapp. Recommend the users that are totally new to the website client novel solution for cross-site cold-start product recommendation that aims for recommending products from e-commerce websites. In specific propose learning both users and products feature representations from data collected from e-commerce websites using recurrent Top-K to transform users social networking features into user embeddings. The survey paper develops a Top-k approach which can manipulate the learnt user implanting for cold-start product recommendation.