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Found 20 papers, 8 papers with code
Augmenting Legal Judgment Prediction with Contrastive Case Relations
Existing legal judgment prediction methods usually only consider one single case fact description as input, which may not fully utilize the information in the data such as case relations and frequency.
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models
To bridge this gap, we introduce OptFusion, a method that automates the learning of fusion, encompassing both the connection learning and the operation selection.
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems
Furthermore, to utilize the global information of the KG, we construct an item-item graph using these semantic embeddings, which can directly capture higher-order associations between items.
End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling
(2) An optimality gap between the two stages results in inferior sub-optimal allocation performance due to the loss of the incentive recommendation information for the uplift prediction under the limited budget constraint.
OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction
In this paper, we propose a novel optimal distribution selection model OptDist for CLTV prediction, which utilizes an adaptive optimal sub-distribution selection mechanism to improve the accuracy of complex distribution modeling.
Masked Random Noise for Communication Efficient Federated Learning
For this purpose, we propose Federated Masked Random Noise (FedMRN), a novel framework that enables clients to learn a 1-bit mask for each model parameter and apply masked random noise (i. e., the Hadamard product of random noise and masks) to represent model updates.
A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation
Specifically, in the information reconstruction stage, we design a new user-level SFT task for collaborative information injection with the assistance of a pre-trained SRS model, which is more efficient and compatible with limited text information.
Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation
In this paper, we study the MTL problem with hybrid targets for the first time and propose the model named Hybrid Targets Learning Network (HTLNet) to explore task dependence and enhance optimization.
Expected Transaction Value Optimization for Precise Marketing in FinTech Platforms
In addition to predicting the click-through rate (CTR) or the conversion rate (CVR) as in traditional recommendations, it is essential for FinTech platforms to estimate the customers' purchase amount for each delivered fund and achieve an effective allocation of impressions based on the predicted results to optimize the total expected transaction value (ETV).
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks.
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features.
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions
Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e. g., recommender systems (RS).
OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction
Existing approaches for multi-scenario CTR prediction generally consist of two main modules: i) a scenario-aware learning module that learns a set of multi-functional representations with scenario-shared and scenario-specific information from input features, and ii) a scenario-specific prediction module that serves each scenario based on these representations.
Explicit Feature Interaction-aware Uplift Network for Online Marketing
Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i. e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario.
Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset
To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to the system-induced biases.
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data.
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Representation learning has been a critical topic in machine learning.
Optimizing Feature Set for Click-Through Rate Prediction
Because of the large-scale search space, we develop a learning-by-continuation training scheme to learn such gates.
Ranked #4 on
Click-Through Rate Prediction
on KDD12
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.
FLEN: Leveraging Field for Scalable CTR Prediction
By suitably exploiting field information, the field-wise bi-interaction pooling captures both inter-field and intra-field feature conjunctions with a small number of model parameters and an acceptable time complexity for industrial applications.
Ranked #15 on
Click-Through Rate Prediction
on Avazu
