TITLE:
A Novel and Hybrid Approach of an Indian Demographic Movie Recommender System
AUTHORS:
G S Ananth, K. Raghuveer, R Dayananda, R Kashyap
KEYWORDS:
Demographic Filtering (DF), Information Retrieval (IR), Recommender Systems (RS), Similarity Index (SI)
JOURNAL NAME:
Open Access Library Journal,
Vol.7 No.7,
July
20,
2020
ABSTRACT:
India is a demographic democratic country having a population of nearing 140 crores and with different people of various religions, communicating numerous languages, wearing different varieties of clothes. India is also a cacophony of languages, with more than 1500 films being produced every year in its 20+ languages. Recommender systems give personalized outputs in the form of the information being processed. But unfortunately, there is very little personalization done or the data available for this voluminous demographic attribute possessed by India. For example, though there are different platforms like Amazon prime videos, Netflix, tickets booked through www.bookmyshow.com/ to watch movies but not restricted to just Hindi and English (the two official languages of India)—there is little concentration towards the demographic data of Indian languages. In this paper, we present a novel way of creating an Indian Demographic Movie Recommender System (IDMRS) making full utilization of the various demographic attributes available. IDMRS is a system capable of filtering and providing personalization to users in five regional south Indian languages. This system makes use of various characteristics and demographic attributes, such as age, gender and occupational details for the generation of recommendations. Also, a curated dataset, similar to MovieLens dataset, is evolved with this system and is evaluated with various performance metrics.