A Hybrid Recommendation Technique for Big Data Systems

Recommender systems are engines that recommend new items to users by analyzing their preferences. The web contains a large amount of information in the form of ratings, reviews, feedback on items and other unstructured data. These details are extracted to get meaningful information of users. Collaborative filtering and content-based filtering are two common approaches being used to make recommendations. The paper aims to introduce a hybrid recommendation technique for Big Data Systems. The approach combines collaborative and content-based filtering techniques to recommend items that a user would most likely prefer. It additionally uses items ranking and classification technique for recommending the items. Moreover, social media opinion mining is added as a top-up to derive user sentiments from user’s posts and become knowledgeable about users’ tastes hidden within social media. A prototype has been implemented and evaluated based on the recommendation techniques.

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