Conference: 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) In recent years, data security has been one of the biggest concerns, and individuals have grown increasingly worried about the security of their personal information. Personalization typically necessitates the collection of individual data for analysis, exposing customers to privacy concerns. Companies create an illusion of safety to make people feel safe using a mainstream word, "encryption". Though encryption protects personal data from an external breach, the companies can still exploit personal data collected from users as they own the encryption keys. We present a naive yet secure approach for recommending movies to consumers without collecting any personally identifiable information. Our proposed approach can assist a movie recommendation system understand user preferences using the user's movie watch-time and watch history only. We conducted a comprehensive and comparative study on the performance of three deep reinforcement learning architectures, namely DQN, DDQN, and D3QN, on the same task. We observed that D3QN outperformed the other two architectures and achieved a precision of 0.880, recall of 0.805, and F1 score of 0.830. The results show that we can build a competitive movie recommendation system using unidentifiable data.

1. A Safer Approach to build Recommendation Systems
on Unidentifiable Data

2. Kishor Datta Gupta* Akib Sadmanee+ Nafiz Sadman+
Authors
* University of Memphis, TN, USA
+ Silicon Orchard Ltd, Bangladesh

3. Roadmap
Introduction
Experiments & Results
Hypothesis
Conclusion
Data Preparation

4. Introduction

5. 01/08
• Privacy is an illusion
• People have become products
• Identifiable information is shared knowingly/unknowingly
• Unaware about identifiable/unidentifiable data
• Recommendation systems exploit user privacy

6. 02/08
How can we build a personalized recommendation system
without personal information?
Identifiable Data Unidentifiable Data

7. 03/08
Our Contribution
A comprehensive and comparative study of deep
reinforcement learning algorithms (DQN, DDQN, D3QN)
for devising a movie recommendation system using
unidentifiable data

8. 04/08
It is possible to build a good enough recommendation system
without using trackers and collecting identifiable sensitive
personal data from the everyday user of the internet.
Hypothesis

9. 04/08
A RL agent can learn to recommend a set of movies to a user
based on only the user’s movie watch-time.
Hypothesis

10. Data preparation

11. 05/08
• Pre-existing datasets comprise identifiable data
like User ID, rating, clicks etc.
• 20 human annotators
• Each annotator was asked to watch 50 movies
• We record the watch time of an annotator watching a
particular movie
• Why not consider movie watching frequency?

12. Experiments & Results

13. 06/08
Q-learning (QN)
Deep Q-learning (DQN)
Double Deep Q-learning (DDQN)
Dueling Double Deep Q-learning (D3QN)
Reward
Reward
Reward
Episodes Episodes Episodes
(a) DQN (b) DDQN (c) D3QN

14. 07/08

15. Conclusion

16. 08/08
D3QN performs the best among the three Deep RL algorithms
It is indeed possible to build a recommendation system without using any
identifiable/sellable data