MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||7|
|Section||Smart Algorithms and Recognition|
|Published online||04 March 2020|
DSSMFM: Combining user and item feature interactions for recommendation systems
Beijing University of Posts and Telecommunications, Beijing, China
* Corresponding author: email@example.com
Effort has been done to optimize machine learning algorithms by applying relevant knowledges in data fields in recommendation systems. Ways are explored to discover the relationship of features independently, making the model more effective and robust. A new model, DSSMFM is proposed in this paper which combines user and item features interactions to improve the performance of recommendation systems. In this model, data are divided into user features and item features represented by one-hot vectors. The pre-training for the model is proceeded through FM, and implicit vectors are obtained for both user and item features. The implicit vectors are used as the input of DSSM, and the training of the DSSM part of the model will maximize the cosine distances of the user attributes vectors and the item attributes vectors. According to the experimental results on dataset of ICME 2019 Short Video Understanding and Recommendation Challenge, the model shows improvements on some results of the baselines.
Key words: DSSM / FM / Feature extraction / User attributes vector / Item attributes vector
© The Authors, published by EDP Sciences, 2020
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