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Because heterogeneous information network（HIN） contains rich network structure and semantic information, recommendation systems often use HIN for recommendation. However, the current researches of recommender systems is mainly based on indirect information provided by meta-paths for recommendation, but these researches don’t make full use of direct interactive information. To make full use of this information, this paper proposed a ternary interaction model（AMMRec） that incorporated attention mechanisms and heterogeneous information network meta-paths. This method firstly used the implicit feedback matrix to construct user similarity matrix and item similarity matrix and used the representation learning method of the HIN to obtain the corresponding feature vector embeddings in HIN. Then it used the the attention mechanism to modify the embeddings and designed attention neural network to fuse representation vectors of different meta-paths. Finally it concatenated user embeddings and meta-path embeddings and item embeddings, and generated recommendation results through fully connected neural network. The experimental results on real datasets show that AMMRec improves the recommendation accuracy by up to 9.5%. In addition, AMMRec has good interpretability for the recommendation results. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the recommendation model cannot mine the diversity of user interest and capture the order information between the sequence of user behavior, and the interaction occurs at the element level rather than between the feature vectors, etc., based on multiple attention mechanism and location information, this paper proposed the x DMAL FM. Firstly, it extracted the feature depth from different subspaces by using the multi-head attention mechanism, and then location information could capture the sequential relationship between user behavior sequences. Finally, it used three public datasets to conduct comparative experiments and the AUC index to evaluate the results. The experimental results show that the proposed algorithm has better recommendation performance than the xDeepFM model and show the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, as deep learning has achieved good results in many fields, deep learning has also begun to be applied to recommendation systems, such as NFM models and DeepFM models that use deep learning technology to capture high-level feature interactions. However, considering the changes in the external environment and internal perception, the user' s interest should also change dynamically over time, and the combination based on the original features may not necessarily learn effective feature interaction. This paper attempted to build a new model FG_DRFwFm, which could learn the interaction of low-level and high-level features of multiple feature domains and dealt with long-term changes in user interest. It constructed the training features by constructing new features based on the original features and splicing them together, which could better learn effective feature interaction. Finally, the proposed model compared the recommendation effect with multiple advanced CTR algorithms on the MovieLens data set. The experimental results show that the proposed model achieves better results. [ABSTRACT FROM AUTHOR]