Cross-domain sequential recommendation method based on time series and projection enhancement

A cross-domain sequential recommendation method based on time series and projection enhancement, comprising the following steps:

• S1, encoding item nodes and timestamps separately, and capturing dependency between mixed-domain items and single-domain items through three directed matrices, wherein the item nodes are encoded through a graph attention mechanism, and interdependency of nodes in a global task is captured, the timestamps are mapped into a high-dimensional space, a time span is represented by using a vector dot product, and the time span is evaluated by comparing similarity in time embeddings, thereby capturing dependency and time characteristic between nodes in three interaction sequences;
• S2, aggregating the item nodes and time nodes by using a multi-head attention mechanism, then extracting shared information of a mixed sequence and unique information of a specific domain through a mapping mechanism-based gated transmission module, and through an auxiliary task, mixing information of a cross-domain sequence to obtain prototype representation of each of single-domains sequences and thereby enhance information features of each of domains;
• S3, processing a cross-domain interaction sequence by using a masking mechanism to obtain sequence representation of each of single-domains, using contrastive learning to enhance representation of the cross-domain sequence, and training on single-domain information extracted from the cross-domain sequence and the single-domain sequences;
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• S4, predicting using the representations of the cross-domain sequence and the single-domain sequences, processing sequences using a softmax function, and then selecting a top-ranked item from the domains' predictions as a next recommended item.