Ti-SSE: a time interval based sequential recommendation model via stochastic shared embeddings

Haoran Xiang; Pengxiang Lan; Yuhao Wang

doi:10.1117/12.2667357

28 March 2023 Ti-SSE: a time interval based sequential recommendation model via stochastic shared embeddings

Haoran Xiang, Pengxiang Lan, Yuhao Wang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125663F (2023) https://doi.org/10.1117/12.2667357
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

Sequential recommendation systems attempt to use the sequence of user interactions to predict users' next behavior according to their recent based on their most recent actions. Markov chains, recurrent neural networks and self-attention have been widely developed because of their traditional ability to capture the dynamics of sequential patterns. However, most of these models make a simplified assumption that they treat the interaction history as an ordered sequence and ignore the time interval between each interaction. In other words, their model is a time series without considering the actual time interval. In this paper, we try to explicitly model the interactive timestamp in a sequence model framework and explore the impact of different time intervals on the prediction of the next project. We propose the model Ti-SSE which combines TiSASRec and SSE-PT model. Thus, we avoid the shortcomings of the traditional sequential recommendation models that ignore the absolute time intervals while the Stochastic Shared Embeddings (SSE) regularization makes our model far from overfitting due to excessive use of self-attention blocks. The results on several datasets show that our model Ti-SSE has advantages over the previous models.

Citation Download Citation

Haoran Xiang, Pengxiang Lan, and Yuhao Wang "Ti-SSE: a time interval based sequential recommendation model via stochastic shared embeddings", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125663F (28 March 2023); https://doi.org/10.1117/12.2667357

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KEYWORDS

Education and training

Transformers

Stochastic processes

Data modeling

Batch normalization

Computer programming

Performance modeling

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