Abstract
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.
Highlights
Feature play an essential role in many recommendation systems, and a good feature brings about significant benefits to the model and algorithm itself
In the huge real scenario of the industry, features are in quantity and complicated, which makes manual feature extraction infeasible
Manual feature extraction substitutes into prior factors, which, to some extent, effaces the relationship between potential features
Summary
Feature play an essential role in many recommendation systems, and a good feature brings about significant benefits to the model and algorithm itself. Different from the traditional machine learning that is a process composed of multiple independent modules which contain complicated feature engineering, some deep learning models are about "end-to-end". In the field of recommendation systems, it is of great significant to learn complicated and selective feature interaction [1] by using DNN. It is hoped to find a way that does not need sundry manual feature engineering, and combines vector interactive representation of low-order feature that are applicable to large and scattered data and highorder feature presentation and powerful interpretability. In this paper, we propose a neural network-based model, DSSMFM-Deep Structured Semantic Model & Factorization Machine, and learned feature interaction through a distinct vectorization way. The potential relationships between user and item were expressed via cosine distance constraints
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