Multi-action Click Prediction Model for Short Video Users Based On User’s Behavior Sequence

GU Yiran; WANG Yu; YANG Haigen

doi:10.11999/JEIT211458

Volume 45 Issue 2

Feb. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(2): 672-679

GU Yiran, WANG Yu, YANG Haigen. Multi-action Click Prediction Model for Short Video Users Based On User’s Behavior Sequence[J]. Journal of Electronics & Information Technology, 2023, 45(2): 672-679. doi: 10.11999/JEIT211458

Citation:

GU Yiran, WANG Yu, YANG Haigen. Multi-action Click Prediction Model for Short Video Users Based On User’s Behavior Sequence[J]. Journal of Electronics & Information Technology, 2023, 45(2): 672-679. doi: 10.11999/JEIT211458

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Multi-action Click Prediction Model for Short Video Users Based On User’s Behavior Sequence

doi: 10.11999/JEIT211458

GU Yiran^{1, 2
,
,},
WANG Yu¹,
YANG Haigen³

1.
College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2.
Center of Smart Campus Research, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3.
Center of Wider and Wireless Communication Technology, Ministry of Education, Nanjing 210003, China

Funds: The Key R&D Program of Ministry of Science and Technology, China (SQ2021YFB3300069)

Received Date: 2021-12-08
Accepted Date: 2022-06-08
Rev Recd Date: 2022-06-07

Available Online: 2022-06-13

Publish Date: 2023-02-07

Abstract

Abstract

At present, the mainstream click prediction model uses the combination of linear model and deep neural network to learn the characteristic interaction between users and items, ignoring the fact that the user’s historical behavior is essentially a dynamic sequence, resulting in the inability to capture effectively the time information contained in the user’s behavior sequence. Therefore, a short video USer multi behavior Click Prediction model (USCP) based on user behavior sequence is proposed in this paper. The model sorts the user’s historical behavior in the order of interaction time, and generates the user’s historical behavior sequence. Based on the DeepFM model, the word embedding model word2vec is introduced to learn adaptively the user’s dynamic interest according to the user’s historical behavior sequence and capture effectively the changes of user interest. A comparative experiment is carried out on the desensitization data set published on a short video platform. The evaluation index adopts GAUC (Group AUC). The results show that the performance of this model is better than other models.
- Behavior sequence,
- Deep learning,
- DeepFM,
- Click prediction,
- Word2Vec

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References(18)

References

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