Cross-Platform-Recommendation-System (Recommendation provided on movies based on Netflix and MovieLens Data)

Mammoth amount of data is scattered across the Internet and surfing in this ocean of data is an endless task. To make user interaction smoother, recommendation systems are used to reduce the overload of information. In single domain recommendation systems, although the item space is humongous, users usually rate only few items and hence these systems focus on users having specific interests rather than relying on the wisdom of the majority. Due to this, they face a variety of problems like cold start, sparsity, issues related to new users, items and so on. In cross platform recommender system (CPRS) , a recommender may draw on information acquired from other domains to alleviate such problems. This is achieved by transferring knowledge available in other platforms (known as the source domain or platform) to the target platform. This not only helps to establish relations between the items but also leads to higher user involvement and better suggestions Here we explore the classic framework of item-item based collaborative filtering using which we propose a CPRS that maps the wisdom from our source platform Netflix to our target platform MovieLens . CPRS aims at achieving better accuracy and adds a new dimension in solving the cold start problem.

UI link : https://crossplatformrecommendationengine.github.io/CPRS/result