Sequential Recommendation with Relation-Aware Kernelized Self-Attention
Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Transformer is a deterministic measure without relation-awareness. Therefore, we introduce a latent space to the self-attention, and the latent space models the recommendation context from relation as a multivariate skew-normal distribution with a kernelized covariance matrix from co-occurrences, item characteristics, and user information. This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics. We experimented RKSA over the benchmark datasets, and RKSA shows significant improvements compared to the recent baseline models. Also, RKSA were able to produce a latent space model that answers the reasons for recommendation.
Code (0)
등록된 구현이 없습니다.
Tasks
RelationSequential RecommendationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Position-Aware Sequential Attention for Accurate Next Item Recommendations
Sequential self-attention models usually rely on additive positional embeddings, which inject positional information into item representations at the input. In the absence of positional signals, the attention block is pe…
Semantic SimilaritySequential Recommendation with Latent Relations based on Large Language Model
Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative …
Collaborative FilteringKnowledge GraphsLanguage ModelingLanguage Modelling+5SANST: A Self-Attentive Network for Next Point-of-Interest Recommendation
Next point-of-interest (POI) recommendation aims to offer suggestions on which POI to visit next, given a user's POI visit history. This problem has a wide application in the tourism industry, and it is gaining an increa…
Sequential RecommendationDisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is…
Contrastive LearningDisentanglementS^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data re…
AttributeSelf-Supervised LearningSequential Recommendation