Toward Dynamic User Intention: Temporal Evolutionary Effects of Item Relations in Sequential Recommendation

User intention is an important factor to be considered for recommender systems, which always changes dynamically in different contexts. Recent studies (represented by sequential recommendation) begin to focus on predicting what users want beyond what users like, which are better at capturing user intention and have attracted a surge of interest. However, user intention modeling is non-trivial, because it is generally influenced by various factors, among which item relations and their temporal evolutionary effects are of great importance. For example, consumption of a cellphone will have varying impacts on the demands for its relational items: For complements, the demands are likely to be promoted in the short term; while for substitutes, the long-term effect may take advantage, because users do not need another cellphone immediately. Moreover, the temporal evolutions of different relational effects vary across different domains, which makes it challenging to adaptively take them into consideration. As a result, most existing studies only loosely incorporate item relations by encoding their semantics into embeddings, neglecting fine-grained time-aware effects. In this work, we propose Knowledge-aware Dynamic Attention (KDA) to take both relational effects and their temporal evolutions into consideration. Specifically, to model dynamic impacts of historical relational interactions on user intention, we aggregate the history sequence into relation-specific embeddings, where the attention weight consists of two parts. First, we measure the relational intensity between historical items and the target item to model the absolute degree of influence in terms of each relation. Second, to model how the relational effects drift with time, we innovatively introduce Fourier transform with learnable frequencydomain embeddings to estimate temporal decay functions of different relations adaptively. Subsequently, the self-attention mechanism is leveraged to derive the final representation of the whole history sequence, which reflects the dynamic user intention and will be applied to generate the recommendation list. Extensive experiments in three real-world datasets indicate the proposed KDA model significantly outperforms the state-of-the-art methods on the Top-K recommendation task. Moreover, the proposed Fourier-based method opens up a new avenue to adaptively integrate temporal dynamics into general neural models.