公开(公告)号：US20230169569A1

公开(公告)日：2023-06-01

申请号：US17813741

申请日：2022-07-20

Applicant: Tata Consultancy Services Limited

Inventor： PRIYANKA GUPTA ,  PANKAJ MALHOTRA ,  ANKIT SHARMA ,  GAUTAM SHROFF ,  LOVEKESH VIG

IPC: G06Q30/06

CPC classification number: G06Q30/0631

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.

公开(公告)号：US20220093249A1

公开(公告)日：2022-03-24

申请号：US17374033

申请日：2021-07-13

Applicant: Tata Consultancy Services Limited

Inventor： ANKIT SHARMA ,  GARIMA GUPTA ,  RANJITHA PRASAD ,  ARNAB CHATTERJEE ,  LOVEKESH VIG ,  GAUTAM SHROFF

IPC: G16H50/20 ,  G16H50/70 ,  G16H70/40 ,  G06N3/08

Abstract: In presence of high-cardinality treatment variables, number of counterfactual outcomes to be estimated is much larger than number of factual observations, rendering the problem to be ill-posed. Furthermore, lack of information regarding the confounders among large number of covariates pose challenges in handling confounding bias. Essential is to find lower-dimensional manifold where an equivalent problem of causal inference can be posed, and counterfactual outcomes can be computed. Embodiments herein provide a method and system for CI in presence of high-dimensional covariates and high-cardinality treatments using Hi-CI DNN architecture comprising Hi-CI DNN model built by concatenating a decorrelation network and a modified regression network for jointly generating low-dimensional decorrelated covariates from the high-dimensional covariates, and predicting a set of outcomes for the input data set having the high-cardinality treatments comprising of the plurality of dosage levels by generating per-dosage level embedding to learn representation of the high-cardinality treatments.