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：US20220373171A1

公开(公告)日：2022-11-24

申请号：US17753779

申请日：2020-10-09

Applicant: Tata Consultancy Services Limited

Inventor： ANIRUDH DEODHAR ,  VISHAL JADHAV ,  ASHIT GUPTA ,  MURALIKRISHNAN RAMANUJAM ,  VENKATARAMANA RUNKANA ,  MUKUL PATIL ,  CHARAN THEJA DHANDA ,  DHANDAPANI SUBRAMANIAM ,  LALITH ROSHANLAL JAIN ,  JOEL THOMSON DIRAVIAM ANDREW ,  PANKAJ MALHOTRA ,  SAI PRASAD PARAMESWRAN

IPC: F22B37/56 ,  G05B13/04 ,  F28G15/00 ,  G05B23/02

Abstract: This disclosure relates generally to a method and system for real time monitoring and forecasting of fouling of an air preheater (APH) in a thermal power plant. The system is deploying a digital replica or digital twin that works in tandem with the real APH of the thermal power plant. The system receives real-time data from one or more sources and provides real-time soft sensing of intrinsic parameters as well as that of health, fouling related parameters of APH. The system is also configured to diagnose the current class of fouling regime and the reasons behind a specific class of fouling regime of the APH. The system is also configured to be used as advisory system that alerts and recommends corrective actions in terms of either APH parameters or parameters controlled through other equipment such as selective catalytic reduction or boiler or changes in operation or design.

公开(公告)号：US20220188899A1

公开(公告)日：2022-06-16

申请号：US17593554

申请日：2020-08-25

Applicant: Tata Consultancy Services Limited

Inventor： PANKAJ MALHOTRA ,  PRIYANKA GUPTA ,  DIKSHA GARG ,  LOVEKESH VIG ,  GAUTAM SHROFF

IPC: G06Q30/06 ,  G06F16/22 ,  G06N3/04

Abstract: This disclosure relates generally to method and system for handling popularity bias in item recommendations. In an embodiment the method includes initializing an item embedding look-up matrix corresponding to items in a sequence of item-clicks with respect to a training data. L2 norm is applied to the item embedding look-up matrix to learn a normalized item embeddings. Using a neural network, a session embeddings corresponding to the sequences of item-clicks is modeled and L2 norm is applied to the session embeddings to obtain a normalized session embeddings. Relevance scores corresponding to each of the plurality of items arc obtained based on similarity between the normalized item embeddings and the normalized session embeddings. A multi-dimensional probability vector corresponding to the relevance scores for the items to be clicked in the sequence is obtained. A list of the items ordered based on the multi-dimensional probability vector is provided as recommendation.