109 research outputs found
AI Education Matters: Lessons from a Kaggle Click-Through Rate Prediction Competition
In this column, we will look at a particular Kaggle.com click-through rate (CTR) prediction competition, observe what the winning entries teach about this part of the machine learning landscape, and then discuss the valuable opportunities and resources this commends to AI educators and their students. [excerpt
Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models
In this work, we introduce the notion of Context-Based Prediction Models. A
Context-Based Prediction Model determines the probability of a user's action
(such as a click or a conversion) solely by relying on user and contextual
features, without considering any specific features of the item itself. We have
identified numerous valuable applications for this modeling approach, including
training an auxiliary context-based model to estimate click probability and
incorporating its prediction as a feature in CTR prediction models. Our
experiments indicate that this enhancement brings significant improvements in
offline and online business metrics while having minimal impact on the cost of
serving. Overall, our work offers a simple and scalable, yet powerful approach
for enhancing the performance of large-scale commercial recommender systems,
with broad implications for the field of personalized recommendations
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
Advertising and feed ranking are essential to many Internet companies such as
Facebook and Sina Weibo. Among many real-world advertising and feed ranking
systems, click through rate (CTR) prediction plays a central role. There are
many proposed models in this field such as logistic regression, tree based
models, factorization machine based models and deep learning based CTR models.
However, many current works calculate the feature interactions in a simple way
such as Hadamard product and inner product and they care less about the
importance of features. In this paper, a new model named FiBiNET as an
abbreviation for Feature Importance and Bilinear feature Interaction NETwork is
proposed to dynamically learn the feature importance and fine-grained feature
interactions. On the one hand, the FiBiNET can dynamically learn the importance
of features via the Squeeze-Excitation network (SENET) mechanism; on the other
hand, it is able to effectively learn the feature interactions via bilinear
function. We conduct extensive experiments on two real-world datasets and show
that our shallow model outperforms other shallow models such as factorization
machine(FM) and field-aware factorization machine(FFM). In order to improve
performance further, we combine a classical deep neural network(DNN) component
with the shallow model to be a deep model. The deep FiBiNET consistently
outperforms the other state-of-the-art deep models such as DeepFM and extreme
deep factorization machine(XdeepFM).Comment: 8 pages,5 figure
Lightweight Boosting Models for User Response Prediction Using Adversarial Validation
The ACM RecSys Challenge 2023, organized by ShareChat, aims to predict the
probability of the app being installed. This paper describes the lightweight
solution to this challenge. We formulate the task as a user response prediction
task. For rapid prototyping for the task, we propose a lightweight solution
including the following steps: 1) using adversarial validation, we effectively
eliminate uninformative features from a dataset; 2) to address noisy continuous
features and categorical features with a large number of unique values, we
employ feature engineering techniques.; 3) we leverage Gradient Boosted
Decision Trees (GBDT) for their exceptional performance and scalability. The
experiments show that a single LightGBM model, without additional ensembling,
performs quite well. Our team achieved ninth place in the challenge with the
final leaderboard score of 6.059065. Code for our approach can be found here:
https://github.com/choco9966/recsys-challenge-2023.Comment: 7 pages, 4 figures, ACM RecSys 2023 Challenge Workshop accepted pape
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