Affiliations: [a] School of Computer Science, Hubei University of Technology, Wuhan, China | [b] Xiaomi Technology (Wuhan) Co., Ltd, Wuhan, China
Correspondence:
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Corresponding author: Luanxuan Liu, School of Computer Science, Hubei University of Technology, Wuhan, China. E-mail: [email protected].
Abstract: In recent years, sequential recommendation has received widespread attention for its role in enhancing user experience and driving personalized content recommendations. However, it also encounters challenges, including the limitations of modeling information and the variability of user preferences. A novel time-aware Long-Short Term Transformer (TLSTSRec) for sequential recommendation is introduced in this paper to address these challenges. TLSTSRec has two major innovative features. (1) Accurate modeling of users is achieved by fully leveraging temporal information. Time information is modeled by creating a trainable timestamp matrix from both the perspectives of time duration and time spectrum. (2) A novel time-aware Transformer model is proposed. To address the inherent variability of user preferences over time, the model combines long-term and short-term temporal information and adjusts the personalized trade-offs between long-term and short-term sequences using adaptive fusion layers. Subsequently, newly designed encoders and decoders are employed to model timestamps and interaction items. Finally, extensive experiments substantiate the effectiveness of TLSTSRec relative to various state-of-the-art sequential recommendation models based on MC/RNN/GNN/SA across a spectrum of widely used metrics. Furthermore, experiments are conducted to validate the rationality of the TLSTSRec structure.
Keywords: Sequential recommendation, transformer, self-attention, time-aware model
