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Leveraging Movie Recommendation Using Fuzzy Emotion Features

  • Mala SaraswatEmail author
  • Shampa Chakraverty
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 799)

Abstract

User generated data like reviews encapsulate bursts of emotions that are generated after reading a book or watching a movie. These emotions garnered from reviews can be used in recommending items from entertainment domain with similar emotions. Till now much work has been done using emotions as discrete features in recommender system. In this paper, we delve deeper to use fuzziness in the emotion categories. The use of emotional features such as love, joy, surprise, anger, sadness and fear has been shown to be effective in identifying items with similar features for recommendations. However, there is a certain degree of vagueness and blurring boundaries between the lexicons of these categorical emotion features that has hitherto been largely ignored. In this paper, we tackle the problem of inherent vagueness in emotional features by proposing a framework for movie recommendation using fuzzy emotion features by taking each emotion category as a linguistic variable. We develop a Mamdani Model to extract fuzzy classification rules for recommending movies from emotions extracted from their corresponding reviews. Results show that Guassian fuzzy model with 5 linguistic variables yield 68.43% F-measure which is 10.5% improvement over the SVM based crisp model for recommending movies.

Keywords

Recommender system Emotions Reviews Linguistic variable Movies Fuzzy rule based system 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Division of Computer Engineering, NSITUniversity of DelhiNew DelhiIndia

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