Abstract

Movie recommender expert systems are valuable tools to provide recommendation services to users. However, the existing movie recommenders are technically lacking in two areas: first, the available movie recommender systems give general recommendations; secondly, existing recommender systems use either quantitative (likes, ratings, etc.) or qualitative data (polarity score, sentiment score, etc.) for achieving the movie recommendations. A novel approach is presented in this paper that not only provides topic-based (fiction, comedy, horror, etc.) movie recommendation but also uses both quantitative and qualitative data to achieve a true and relevant recommendation of a movie relevant to a topic. The used approach relies on SentiwordNet and tf-idf similarity measures to calculate the polarity score from user reviews, which represent the qualitative aspect of likeness of a movie. Similarly, three quantitative variables (such as likes, ratings, and votes) are used to get final a recommendation score. A fuzzy logic module decides the recommendation category based on this final recommendation score. The proposed approach uses a big data technology, “Hadoop” to handle data diversity and heterogeneity in an efficient manner. An Android application collaborates with a web-bot to use recommendation services and show topic-based recommendation to users.

Highlights

  • Since the advent of web intelligence, artificial intelligence-based services, frameworks and products have become popular in the World Wide Web

  • This paper proposes an intelligent and automated recommendation system that provides two-fold novelty

  • This paper presented an intelligent and automated recommender system to provide topic based, accurate recommendations of movies to users

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Summary

Introduction

Since the advent of web intelligence, artificial intelligence-based services, frameworks and products have become popular in the World Wide Web. Recommendation systems have become popular in the domain of movies, music, books, restaurants, garments, mobile applications and many other fields of life. Such recommendation systems filter huge amount of structured and unstructured data and predict the preference of a user that one would give to an item. Our recommender system uses a multi-variant popularity matrix to recommend a suitable movie to a user on the basis of both quantitative and qualitative variables to achieve true recommendations. A fuzzy logic-based module provides the final recommendation of movies in a particular field of user’s choice (such as comedy, action, horror, fiction, etc.), whereas the currently available systems give general recommendations

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