Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Sequential recommendation methods play a critical role in modern recommender systems because of their ability to capture the context of users' interactions based on their behaviours performed recently. Despite their success, we argue that these sequential recommendation approaches usually consider the relative order of items in a sequence and ignore important time interval information. The time interval can reflect timely changes in user interests by capturing more accurate trends in the evolution of the interests. The ignorance of time intervals will make it difficult for them to learn high-quality user representation. To tackle that, in this paper, we propose a time interval-sensitive mechanism for the sequential recommendation, and we incorporate this mechanism in GRU, named it 2Gated-TimeGRU. We utilise time intervals between two consecutive user interactions as an additional model feature, which enables more accurate modelling of user short-term preferences and temporal dynamics. Experiments on real-world datasets show that the proposed approach outperforms the state-of-the-art baseline models in capturing sequential user preferences and recommendation accuracy.
Recommended Citation
Sun, Ruoxi; Yan, Jun; Ren, Fenghui; and Cao, Cong, "Improving the Accuracy of Sequential Recommendation Using a Time-aware Architecture" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 10.
https://aisel.aisnet.org/hicss-57/da/data_science/10
Improving the Accuracy of Sequential Recommendation Using a Time-aware Architecture
Hilton Hawaiian Village, Honolulu, Hawaii
Sequential recommendation methods play a critical role in modern recommender systems because of their ability to capture the context of users' interactions based on their behaviours performed recently. Despite their success, we argue that these sequential recommendation approaches usually consider the relative order of items in a sequence and ignore important time interval information. The time interval can reflect timely changes in user interests by capturing more accurate trends in the evolution of the interests. The ignorance of time intervals will make it difficult for them to learn high-quality user representation. To tackle that, in this paper, we propose a time interval-sensitive mechanism for the sequential recommendation, and we incorporate this mechanism in GRU, named it 2Gated-TimeGRU. We utilise time intervals between two consecutive user interactions as an additional model feature, which enables more accurate modelling of user short-term preferences and temporal dynamics. Experiments on real-world datasets show that the proposed approach outperforms the state-of-the-art baseline models in capturing sequential user preferences and recommendation accuracy.
https://aisel.aisnet.org/hicss-57/da/data_science/10