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Predicting user demographics based on interest analysis in movie dataset

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Abstract

These days, due to the increasing amount of information generated on the web, most web service providers try to personalize their services. Users also interact with web-based systems in multiple ways and state their interests and preferences by rating the provided items. In this paper, we propose a framework to predict users’ demographic based on ratings registered by users in a system. To the best of our knowledge, this is the first time that the item ratings are employed for users’ demographic prediction problem, which has extensively been studied in recommendation systems and service personalization. We apply the framework to Movielens dataset’s ratings and predict users’ age and gender. The experimental results show that using all ratings registered by users improves the prediction accuracy by at least 16% compared with previously studied models. Moreover, by classifying the items as popular and unpopular, we eliminate ratings belong to 95% of items and still reach an acceptable level of accuracy. This significantly reduces update cost in a time-varying environment. Besides this classification, we propose other methods to reduce data volume while keeping the predictions accurate.

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Data Availability

The data used in this research is readily available online and has been cited appropriately within the text of this paper. Specifically, we utilized two publicly accessible datasets: the Internet Movie Database (IMDb) dataset and the MovieLens dataset. The IMDb and MovieLens dataset can be accessed through the mentioned links in Section 2.1. Detailed information on our utilization of these datasets, including data preprocessing and citation, is provided in the relevant sections of this paper. Researchers interested in reproducing or further exploring our findings are encouraged to refer to these sources for access to the data used in this study.

Notes

  1. https://grouplens.org/datasets/movielens/

  2. https://www.imdb.com/

  3. https://www.motionpictures.org/film-ratings/

  4. http://bit.ly/3ulZch1

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Authors and Affiliations

  1. Faculty of Computer Engineering, University of Isfahan, Azadi Sq., Hezarjarib St., Isfahan, Iran

    Reza Shafiloo & Marjan Kaedi

  2. UNSW, Sydney, Australia

    Ali Pourmiri

Authors
  1. Reza Shafiloo
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  2. Marjan Kaedi
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  3. Ali Pourmiri
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Correspondence to Marjan Kaedi.

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Shafiloo, R., Kaedi, M. & Pourmiri, A. Predicting user demographics based on interest analysis in movie dataset. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18422-6

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