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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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.
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.
The framework can partition data into custom designed time buckets to capture the interactions among information aggregated in different time buckets.
Moreover, it is difficult for user to jump out of their specific historical behaviors for possible interest exploration, namely weak generalization problem.
The FmFM model's performance is also comparable to DNN models which require much more FLOPs in runtime.
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.
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.
Ranked #1 on Click-Through Rate Prediction on Criteo
Concretely, STAR has the star topology, which consists of the shared centered parameters and domain-specific parameters.
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.