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：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.