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Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.

( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )

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Datasets

Latest papers without code

Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph

30 Apr 2021

We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.

CLICK-THROUGH RATE PREDICTION KNOWLEDGE GRAPH EMBEDDING RECOMMENDATION SYSTEMS

Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning

15 Apr 2021

Despite the rapid growth of online advertisement in developing countries, existing highly over-parameterized Click-Through Rate (CTR) prediction models are difficult to be deployed due to the limited computing resources.

CLICK-THROUGH RATE PREDICTION

A Non-sequential Approach to Deep User Interest Model for Click-Through Rate Prediction

5 Apr 2021

The framework can partition data into custom designed time buckets to capture the interactions among information aggregated in different time buckets.

CLICK-THROUGH RATE PREDICTION

Graph Intention Network for Click-through Rate Prediction in Sponsored Search

30 Mar 2021

Moreover, it is difficult for user to jump out of their specific historical behaviors for possible interest exploration, namely weak generalization problem.

CLICK-THROUGH RATE PREDICTION GRAPH LEARNING

$FM^2$: Field-matrixed Factorization Machines for Recommender Systems

20 Feb 2021

The FmFM model's performance is also comparable to DNN models which require much more FLOPs in runtime.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

HSR: Hyperbolic Social Recommender

15 Feb 2021

With the prevalence of online social media, users' social connections have been widely studied and utilized to enhance the performance of recommender systems.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

Learning a Product Relevance Model from Click-Through Data in E-Commerce

14 Feb 2021

We propose a novel way to consider samples of different relevance confidence, and come up with a new training objective to learn a robust relevance model with desirable score distribution.

CLICK-THROUGH RATE PREDICTION

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

9 Feb 2021

We also turn the feed-forward layer in DNN model into a mixture of addictive and multiplicative feature interactions by proposing MaskBlock in this paper.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction

27 Jan 2021

Concretely, STAR has the star topology, which consists of the shared centered parameters and domain-specific parameters.

CLICK-THROUGH RATE PREDICTION

Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction

11 Jan 2021

To solve this problem, in this paper, we present a novel Disentangled Self-Attentive neural Network (DSAN) model for CTR prediction, which disentangles the two terms for facilitating learning feature interactions.

CLICK-THROUGH RATE PREDICTION