no code implementations • 3 Dec 2024 • Luyi Ma, Aashika Padmanabhan, Anjana Ganesh, Shengwei Tang, Jiao Chen, Xiaohan Li, Lalitesh Morishetti, Kaushiki Nag, Malay Patel, Jason Cho, Sushant Kumar, Kannan Achan
Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shopping.
no code implementations • 16 Oct 2024 • Luyi Ma, Xiaohan Li, Zezhong Fan, Jianpeng Xu, Jason Cho, Praveen Kanumala, Kaushiki Nag, Sushant Kumar, Kannan Achan
The LLM models the user's interactions including behaviors and item features in natural languages.
no code implementations • 18 Sep 2024 • Najmeh Forouzandehmehr, Nima Farrokhsiar, Ramin Giahi, Evren Korpeoglu, Kannan Achan
This paper presents a novel framework that harnesses the expressive power of large language models (LLMs) for this task, mitigating their "black box" and static nature through fine-tuning and direct feedback integration.
no code implementations • 11 Sep 2024 • Shanu Vashishtha, Abhay Kumar, Lalitesh Morishetti, Kaushiki Nag, Kannan Achan
We refer to our innovative methodology as Dynamic Text Snippets (DTS) which generates multiple header texts for an anchor item and its recall set.
no code implementations • 17 Apr 2024 • Zezhong Fan, Xiaohan Li, Chenhao Fang, Topojoy Biswas, Kaushiki Nag, Jianpeng Xu, Kannan Achan
The dataset is created with GPT-4 to extend the abstract concept to a scene and concrete objects.
no code implementations • 29 Feb 2024 • Chenhao Fang, Xiaohan Li, Zezhong Fan, Jianpeng Xu, Kaushiki Nag, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry.
no code implementations • 28 Feb 2024 • Shanu Vashishtha, Abhinav Prakash, Lalitesh Morishetti, Kaushiki Nag, Yokila Arora, Sushant Kumar, Kannan Achan
Recent literature has surveyed the use of text-to-image models for enhancing the work of many creative artists.
no code implementations • 5 Feb 2024 • Luyi Ma, Nimesh Sinha, Parth Vajge, Jason HD Cho, Sushant Kumar, Kannan Achan
Product recommendations for the multiple aspects of the target event are usually generated by human curators who manually identify the aspects and select a list of aspect-related products (i. e., product carousel) for each aspect as recommendations.
no code implementations • 2 Feb 2024 • Najmeh Forouzandehmehr, Yijie Cao, Nikhil Thakurdesai, Ramin Giahi, Luyi Ma, Nima Farrokhsiar, Jianpeng Xu, Evren Korpeoglu, Kannan Achan
The outfit generation problem involves recommending a complete outfit to a user based on their interests.
no code implementations • 26 Dec 2023 • Luyi Ma, Nikhil Thakurdesai, Jiao Chen, Jianpeng Xu, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Although the UDF design pattern introduces flexibility, reusability and scalability, the increasing demand on machine learning pipelines brings three new challenges to this design pattern -- not low-code, not dependency-free and not knowledge-aware.
no code implementations • 6 Dec 2023 • Zikun Ye, Reza Yousefi Maragheh, Lalitesh Morishetti, Shanu Vashishtha, Jason Cho, Kaushiki Nag, Sushant Kumar, Kannan Achan
This paper aims to investigate and achieve seller-side fairness within online marketplaces, where many sellers and their items are not sufficiently exposed to customers in an e-commerce platform.
no code implementations • 1 Dec 2023 • Reza Yousefi Maragheh, Chenhao Fang, Charan Chand Irugu, Parth Parikh, Jason Cho, Jianpeng Xu, Saranyan Sukumar, Malay Patel, Evren Korpeoglu, Sushant Kumar, Kannan Achan
We call our LLM-based framework Theme-Aware Keyword Extraction (LLM TAKE).
no code implementations • 26 Oct 2023 • Ramin Giahi, Reza Yousefi Maragheh, Nima Farrokhsiar, Jianpeng Xu, Jason Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Similar item recommendation is a critical task in the e-Commerce industry, which helps customers explore similar and relevant alternatives based on their interested products.
no code implementations • 17 May 2023 • Jiao Chen, Luyi Ma, Xiaohan Li, Nikhil Thakurdesai, Jianpeng Xu, Jason H. D. Cho, Kaushiki Nag, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complementary or substitutable relations between products or product types, which can be utilized in recommender systems.
no code implementations • 16 Nov 2022 • Xiaohan Li, Zheng Liu, Luyi Ma, Kaushiki Nag, Stephen Guo, Philip Yu, Kannan Achan
Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram.
no code implementations • 19 Oct 2022 • Shuyuan Xu, Da Xu, Evren Korpeoglu, Sushant Kumar, Stephen Guo, Kannan Achan, Yongfeng Zhang
Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact.
no code implementations • 11 Feb 2022 • Luyi Ma, Jianpeng Xu, Jason H. D. Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan
On the other hand, the model evaluation will not be trustworthy if the labels for evaluation are not reflecting the true complementary relatedness.
no code implementations • 30 Nov 2021 • Shashank Kedia, Aditya Mantha, Sneha Gupta, Stephen Guo, Kannan Achan
We propose eBERT, a sequence-to-sequence approach by further pre-training the BERT embeddings on an e-commerce product description corpus, and then fine-tuning the resulting model to generate short, natural, spoken language titles from input web titles.
no code implementations • 28 Nov 2021 • Xiaohan Li, Zhiwei Liu, Stephen Guo, Zheng Liu, Hao Peng, Philip S. Yu, Kannan Achan
In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.
1 code implementation • 23 Oct 2021 • Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Selecting the optimal recommender via online exploration-exploitation is catching increasing attention where the traditional A/B testing can be slow and costly, and offline evaluations are prone to the bias of history data.
no code implementations • 23 Oct 2021 • Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
The recent work by Rendle et al. (2020), based on empirical observations, argues that matrix-factorization collaborative filtering (MCF) compares favorably to neural collaborative filtering (NCF), and conjectures the dot product's superiority over the feed-forward neural network as similarity function.
no code implementations • 15 Apr 2021 • Ramasubramanian Balasubramanian, Venugopal Mani, Abhinav Mathur, Sushant Kumar, Kannan Achan
Category recommendation for users on an e-Commerce platform is an important task as it dictates the flow of traffic through the website.
2 code implementations • ICLR 2021 • Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Sequential deep learning models such as RNN, causal CNN and attention mechanism do not readily consume continuous-time information.
no code implementations • 24 Feb 2021 • Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
The generalization performance in the downstream machine learning task is controlled by the alignment between the embeddings and the product relatedness measure.
no code implementations • 12 Dec 2020 • Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Korpeoglu, Kannan Achan, Jingrui He
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.
no code implementations • 8 Dec 2020 • Aditya Mantha, Anirudha Sundaresan, Shashank Kedia, Yokila Arora, Shubham Gupta, Gaoyang Wang, Praveenkumar Kanumala, Stephen Guo, Kannan Achan
In production, our system resulted in an improvement in item discovery, an increase in online engagement, and a significant lift on add-to-carts (ATCs) per visitor on the homepage.
no code implementations • 4 Dec 2020 • Behzad Shahrasbi, Venugopal Mani, Apoorv Reddy Arrabothu, Deepthi Sharma, Kannan Achan, Sushant Kumar
In this work, we propose a semi-supervised attack detection algorithm to identify the malicious datapoints.
no code implementations • 2 Dec 2020 • Venugopal Mani, Ramasubramanian Balasubramanian, Sushant Kumar, Abhinav Mathur, Kannan Achan
Recommender Systems have become an integral part of online e-Commerce platforms, driving customer engagement and revenue.
1 code implementation • NeurIPS 2020 • Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
The feedback data of recommender systems are often subject to what was exposed to the users; however, most learning and evaluation methods do not account for the underlying exposure mechanism.
no code implementations • 2 Nov 2020 • Rahul Radhakrishnan Iyer, Praveenkumar Kanumala, Stephen Guo, Kannan Achan
Searching for and making decisions about products is becoming increasingly easier in the e-commerce space, thanks to the evolution of recommender systems.
1 code implementation • 22 Oct 2020 • Zhiwei Liu, Xiaohan Li, Ziwei Fan, Stephen Guo, Kannan Achan, Philip S. Yu
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket.
no code implementations • 23 Jul 2020 • Mansi Ranjit Mane, Shashank Kedia, Aditya Mantha, Stephen Guo, Kannan Achan
Through recent advancements in speech technology and introduction of smart devices, such as Amazon Alexa and Google Home, increasing number of users are interacting with applications through voice.
5 code implementations • ICLR 2020 • Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.
1 code implementation • 14 Jan 2020 • Zhiwei Liu, Mengting Wan, Stephen Guo, Kannan Achan, Philip S. Yu
By defining a basket entity to represent the basket intent, we can model this problem as a basket-item link prediction task in the User-Basket-Item~(UBI) graph.
2 code implementations • NeurIPS 2019 • Da Xu, Chuanwei Ruan, Sushant Kumar, Evren Korpeoglu, Kannan Achan
To bridge the gap between modelling time-independent and time-dependent event sequence, we introduce a functional feature map that embeds time span into high-dimensional spaces.
no code implementations • 28 Nov 2019 • Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce.
no code implementations • 24 Oct 2019 • Aditya Mantha, Yokila Arora, Shubham Gupta, Praveenkumar Kanumala, Zhiwei Liu, Stephen Guo, Kannan Achan
In this paper, we introduce a production within-basket grocery recommendation system, RTT2Vec, which generates real-time personalized product recommendations to supplement the user's current grocery basket.
1 code implementation • 26 Aug 2019 • Mansi Ranjit Mane, Stephen Guo, Kannan Achan
We propose a novel learning framework to answer questions such as "if a user is purchasing a shirt, what other items will (s)he need with the shirt?"
no code implementations • 16 Mar 2019 • Da Xu, Chuanwei Ruan, Jason Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness.
no code implementations • 6 Mar 2019 • Da Xu, Chuanwei Ruan, Kamiya Motwani, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Here we propose a unified generative graph convolutional network that learns node representations for all nodes adaptively in a generative model framework, by sampling graph generation sequences constructed from observed graph data.
no code implementations • 22 Aug 2018 • Abhimanyu Mitra, Kannan Achan, Sushant Kumar
From the randomized experiment, we learn the feature representations which divide the population into subpopulations where we observe statistically significant difference in average customer feedback between those who were subjected to the intervention and those who were not, with a level of significance l, where l is a configurable parameter in our model.
no code implementations • NeurIPS 2008 • Michael Isard, John Maccormick, Kannan Achan
Continuously-Adaptive Discretization for Message-Passing (CAD-MP) is a new message-passing algorithm employing adaptive discretization.