Recommendation algorithm based on adversarial learning and bidirectional long-short-term memory network

Abstract

The invention relates to a recommendation algorithm based on adversarial learning and a bidirectional long-short-term memory network, which comprises the following steps of: 1, predefining a symbol, including A1) defining a heterogeneous information network, A2) defining a path in the heterogeneous information network, A3) in the heterogeneous information network G, defining a node connection sequence from a user u to an article i as a path, and defining that p = [v1, v2,..., vl], and p belongs to P; and 2, modeling as following: S1, modeling an embedded layer, and representing the embedded layer by using an initialized node vector; S2, constructing a sequence modeling layer, using the vector representation obtained through initialization in the step S1 as input and applying the input to an existing bidirectional LSTM model based on an attention mechanism to optimize vector representation of the node, and learning a coefficient matrix and an offset vector in the model; S3, setting a prediction layer, and finally calculating the probability; and S4, constructing an adversarial learning model. According to the method, the problem of node relation noise in the heterogeneous network isrelieved by learning the adversarial regularization item, adding the adversarial regularization item into a loss function and optimizing the model, the robustness of node embedding is improved, and the recommendation accuracy is ensured.

Description

technical field [0001] The invention relates to the technical field of heterogeneous network recommendation, in particular to a recommendation algorithm based on confrontational learning and bidirectional long-short-term memory network. Background technique [0002] Networks can organize all kinds of data in our lives, such as social networks, biological networks, transportation networks, and so on. Objects and interactions in the real world are often multi-modal and multi-type. To capture and exploit such node and link heterogeneity, heterogeneous networks are proposed and widely used in many practical network mining scenarios, especially in recommender systems. Recommendation models based on heterogeneous networks have attracted extensive attention from researchers because they contain various types of nodes and edges. This type of model can not only alleviate the data sparsity problem in the recommendation system, but also improve the accuracy of the recommendation syst...

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Problems solved by technology

The reasons for the above limitations are: 1) Both the previous node and the subsequent node on the path may have an impact on the node representation

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