Research Area:  Machine Learning
Numerous state-of-the-art recommendation frameworks employ deep neural networks in Collaborative Filtering (CF). In this paper, we propose a cross feature fusion neural network (CFFNN) for the enhancement of CF. Existing studies overlook either user preferences for various item features or the relationship between item features and user features. To solve this problem, we construct a cross feature fusion network to enable the fusion of user features and item features as well as a self-attention network to determine users preferences for items. Specifically, we design a feature extraction layer with multiple MLP (Multilayer Perceptrons) modules to extract both user features and item features. Then, we introduce a cross feature fusion mechanism for an accurate determination of the relationship between different user-item interactions. The features of users and items are crossly embedded and then fed into a prediction network. The attention mechanism enables the model to focus on more effective features. The effectiveness of CFFNN model is demonstrated through extensive experiments on four real-world datasets. The experimental results indicate that CFFNN significantly outperforms the existing state-of-the-art models, with a relative improvement of 3.0\% to 12.1\% on hit ratio (HR) and normalized discounted cumulative gain (NDCG) compared with the baselines.
Author(s) Name:  Ruiyun Yu; Dezhi Ye; Zhihong Wang; Biyun Zhang; Ann Move Oguti; Jie Li; Bo Jin; Fadi Kurdahi
Journal name:  IEEE Transactions on Knowledge and Data Engineering ( Early Access )
Publisher name:  IEEE
Volume Information:  Page(s): 1 - 1
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9312492