Multi-pattern time-aware sequential recommendation with data augmentation
In sequential recommendation systems,explicit user interaction sequences are used as context to infer the user's next possible action.Time-aware sequential recommendation models explore the temporal information within the sequence and consider the impact of time on user decisions.However,existing time-aware sequential recommen-dation models only utilize raw temporal information,while many additional pieces of information in the original se-quence are not fully exploited,such as user ratings,item attributes,item popularity,and textual information like item titles and reviews.Therefore,the DMTiSASRec model was proposed,which not only efficiently extracted relevant or-ders beyond temporal information but also leveraged techniques like contrastive learning and multi-modal methods to mine different types of additional information.Experiments on five publicly available datasets across different do-mains and scales show that DMTiSASRec outperforms existing models in terms of effectiveness.
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