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Found 15 papers, 4 papers with code
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Due to limited user interactions for each product (i. e. item), the corresponding item embedding in the CTR model may not easily converge.
Effective Two-Stage Knowledge Transfer for Multi-Entity Cross-Domain Recommendation
In the end, the extracted common knowledge is adopted for target entity model training.
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving.
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training.
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao
Another line of work relies on costly uniform data that is inadequate to train industrial models.
COPR: Consistency-Oriented Pre-Ranking for Online Advertising
In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles more candidates with strict latency requirements.
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach
However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions.
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
However, far less attention has been paid to the overfitting problem of models in recommendation systems, which, on the contrary, is recognized as a critical issue for deep neural networks.
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
To alleviate this issue, we propose to extract knowledge from the \textit{super-domain} that contains web-scale and long-time impression data, and further assist the online recommendation task (downstream task).
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the click probability.
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
We further show that such a process is equivalent to adding an adversarial perturbation to the model input, and thereby name our proposed approach as an the Adversarial Gradient Driven Exploration (AGE).
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution.
RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble
In this paper, we report the results of our participation in the TREC-COVID challenge.
Learning-to-Rank with BERT in TF-Ranking
This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance.
Deep Keyphrase Generation
Keyphrase provides highly-condensed information that can be effectively used for understanding, organizing and retrieving text content.
