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Found 15 papers, 3 papers with code
Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion
Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach.
Explainable Recommender with Geometric Information Bottleneck
Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems.
Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling
In this manuscript (ms), we propose causal inference based single-branch ensemble trees for uplift modeling, namely CIET.
On the Sparse DAG Structure Learning Based on Adaptive Lasso
Learning the underlying Bayesian Networks (BNs), represented by directed acyclic graphs (DAGs), of the concerned events from purely-observational data is a crucial part of evidential reasoning.
Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network
Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods.
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning
To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation.
Learning to Hash Naturally Sorts
In this paper, we tackle this problem by introducing Naturally-Sorted Hashing (NSH).
You Never Cluster Alone
On one hand, with the corresponding assignment variables being the weight, a weighted aggregation along the data points implements the set representation of a cluster.
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.
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.
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)
Modeling Dynamic Missingness of Implicit Feedback for Recommendation
Recent studies modeled \emph{exposure}, a latent missingness variable which indicates whether an item is missing to a user, to give each missing entry a confidence of being negative feedback.
Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality.
Dynamic Decision Process Modeling and Relation-line Handling in Distributed Cooperative Modeling System
First, the thesis presents a discussion of characteristics and optimal policy finding Markov Decision Process as well as a brief introduction to dynamic Bayesian decision network, which is inherently equal to MDP.
An Incidence Geometry approach to Dictionary Learning
We study the Dictionary Learning (aka Sparse Coding) problem of obtaining a sparse representation of data points, by learning \emph{dictionary vectors} upon which the data points can be written as sparse linear combinations.
