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Found 15 papers, 4 papers with code
Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce
Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems.
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
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.
Causal Structure Learning with Recommendation System
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.
Generating Rich Product Descriptions for Conversational E-commerce Systems
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.
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
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.
PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer
Online Learning to Rank (OL2R) eliminates the need of explicit relevance annotation by directly optimizing the rankers from their interactions with users.
A Real-Time Whole Page Personalization Framework for E-Commerce
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.
An End-to-End ML System for Personalized Conversational Voice Models in Walmart E-Commerce
Searching for and making decisions about products is becoming increasingly easier in the e-commerce space, thanks to the evolution of recommender systems.
Basket Recommendation with Multi-Intent Translation Graph Neural Network
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket.
Product Title Generation for Conversational Systems using BERT
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.
BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network
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.
A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations
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.
Complementary-Similarity Learning using Quadruplet Network
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?"
Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing
We introduce a deep learning approach to overcome the drawbacks by applying a large training data set, auto feature selection and fast, accurate labeling.
