Search Results for author:
Found 14 papers, 6 papers with code
Modeling User Behavior with Graph Convolution for Personalized Product Search
Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences.
Embedding-based Product Retrieval in Taobao Search
We evaluate MGDSPR on Taobao Product Search with significant metrics gains observed in offline experiments and online A/B tests.
AutoDebias: Learning to Debias for Recommendation
This provides a valuable opportunity to develop a universal solution for debiasing, e. g., by learning the debiasing parameters from data.
Learning a Product Relevance Model from Click-Through Data in E-Commerce
We propose a novel way to consider samples of different relevance confidence, and come up with a new training objective to learn a robust relevance model with desirable score distribution.
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems
Most existing methods can be categorized as \emph{multi-view representation fusion}; they first build one graph and then integrate multi-view data into a single compact representation for each node in the graph.
AliCoCo: Alibaba E-commerce Cognitive Concept Net
However, user needs in e-commerce are still not well defined, and none of the existing ontologies has the enough depth and breadth for universal user needs understanding.
Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs
We generalize the propagation strategies of current GCNs as a \emph{"Sink$\to$Source"} mode, which seems to be an underlying cause of the two challenges.
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning
In addition, our methods are based on the multi-task learning framework and mitigate the data sparsity issue.
Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
Although existing methods, typically built on the user sequential behavior path ``impression$\to$click$\to$purchase'', is effective for dealing with SSB issue, they still struggle to address the DS issue due to rare purchase training samples.
Conceptualize and Infer User Needs in E-commerce
Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications.
SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors.
User Validation of Recommendation Serendipity Metrics
Though it has been recognized that recommending serendipitous (i. e., surprising and relevant) items can be helpful for increasing users' satisfaction and behavioral intention, how to measure serendipity in the offline environment is still an open issue.
Deep Session Interest Network for Click-Through Rate Prediction
Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt, Deep Session Interest Network(DSIN)
Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System
The deep cascade structure and the combination rule enable the proposed \textit{ldcTree} to have a stronger distributed feature representation ability.
