TiCoSeRec: Augmenting Data to Uniform Sequences by Time Intervals for Effective Recommendation

• Published in: IEEE transactions on knowledge and data engineering Vol. 36; no. 6; pp. 2686 - 2700
• Main Authors: Dang, Yizhou, Yang, Enneng, Guo, Guibing, Jiang, Linying, Wang, Xingwei, Xu, Xiaoxiao, Sun, Qinghui, Liu, Hong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Contrastive learning; Data augmentation; Data models; Datasets; Intervals; recommender systems; sequential recommendation; Skin; Task analysis; time intervals; Training; Transformers; Transforms
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2023.3324312

Sequential recommendation has now been more widely studied, characterized by its well-consistency with real-world recommendation situations. Most existing works model user preference as the transition pattern from the previous item to the next, ignoring the time interval between these two items. However, we find that the time intervals in different sequences may vary significantly and thus result in the ineffectiveness of user modeling due to the issue of preference drift . Thus we propose an assumption that a sequence with uniformly distributed time intervals (denoted as uniform sequence) is more beneficial for preference learning than that with greatly varying time intervals. We then conduct an empirical study on four real datasets and the results support this assumption. Therefore, we advocate to augment sequence data from the perspective of time intervals, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-CateReorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths and a looseness range to ensure the generalization (or diversity) of generated data. Finally, we implement these improvements on a state-of-the-art model CoSeRec and propose Ti me I nterval Aware CoSeRec (TiCoSeRec). Experimental results on four datasets demonstrate that TiCoSeRec achieves significantly better performance than other 11 counterparts recommendation techniques.