FLEX: A Content Based Movie Recommender
Rujhan Singla, Saamarth Gupta, Anirudh Gupta, Dinesh Kumar Vishwakarma
Abstract
Recommender systems are an efficient and powerful method for enabling users to filter through large information and product spaces. In this paper, we present a movie recommendation framework (FLEX) following a content based filtering approach. FLEX extends existing approaches like Doc2Vec and tf-idf by using a hybrid of the two methods. We use publicly available features such as movie plots, ratings, countries of production and release year to find similarity between movies and generate a recommendation list. By providing recommendations which are consistent with customer interests, the aim is to make a platform personalised and ensure customer engagement.
Topics & Concepts
FLEXRecommender systemComputer scienceSimilarity (geometry)Collaborative filteringFilter (signal processing)World Wide WebInformation retrievalProduct (mathematics)MultimediaArtificial intelligenceImage (mathematics)MathematicsTelecommunicationsGeometryComputer visionVideo Analysis and SummarizationRecommender Systems and TechniquesImage Retrieval and Classification Techniques