System, method, and non-transitory machine-readable information storage medium for recommendations of items and controlling an associated bias thereof

US12175520B2 · kind B2 · utility

Assignee

• Tata Consultancy Services Limited · IN

Inventors

• Priyanka Gupta · Sandol, IN
• Pankaj Malhotra · Noida, IN
• Ankit Sharma · Dubai, AE
• Gautam Shroff · Manesar, IN
• Lovekesh Vig · Bhangrola, IN

Key dates

• Filing date: Jul 20, 2022
• Grant date: Dec 24, 2024
• Expiry date: Apr 2, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/0282
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Recommender Systems (RS) tend to recommend more popular items instead of the relevant long-tail items. Mitigating such popularity bias is crucial to ensure that less popular but relevant items are recommended. System described herein analyses popularity bias in session-based RS obtained via deep learning (DL) models. DL models trained on historical user-item interactions in session logs (having long-tailed item-click distributions) tend to amplify popularity bias. To understand source of this bias amplification, potential sources of bias at data-generation stage (user-item interactions captured as session logs) and model training stage are considered by the system for recommendation wherein popularity of item has causal effect on user-item interactions via conformity bias, and item ranking from models via biased training process due to class imbalance. While most existing approaches address only one of these effects, a comprehensive causal inference framework is implemented by present disclosure that identifies and mitigates effects at both stages.