Search Results for author:
Found 15 papers, 6 papers with code
Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction
Online travel platforms (OTPs), e. g., Ctrip. com or Fliggy. com, can effectively provide travel-related products or services to users.
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training
E-commerce search engines comprise a retrieval phase and a ranking phase, where the first one returns a candidate product set given user queries.
Re-weighting Negative Samples for Model-Agnostic Matching
Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry.
Intelligent Request Strategy Design in Recommender System
RSs typically put a large number of items into one page to reduce excessive resource consumption from numerous paging requests, which, however, would diminish the RSs' ability to timely renew the recommendations according to users' real-time interest and lead to a poor user experience.
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.
IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search
On this basis, we develop a specific interactive hypergraph neural network to explicitly encode the structure information (i. e., collaborative signal) into the embedding process.
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms.
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.
Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
Motivated by this observation, we propose a novel \emph{CVR} prediction method by Hierarchically Modeling both Micro and Macro behaviors ($HM^3$).
XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System
The offline experimental results based on real-world E-commerce data demonstrate the effectiveness and verify the importance of unclicked items in sequential recommendation.
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction.
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph.
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.
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.
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)
