Sequential Recommendation Task
(Redirected from Sequential Recommendation)
Jump to navigation
Jump to search
A Sequential Recommendation Task is an item recommendation task that ...
- Context:
- It can be solved by a System (that implements a sequence-aware recommendation algorithm).
- See: Memoryless Item Recommendation.
References
2019
- (Sun, Liu et al., 2019) ⇒ Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. (2019). “BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer.” In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM-2019).
- QUOTE: ... Modeling users’ dynamic preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural networks to encode users’ historical interactions from left to right into hidden representations for making recommendations. Despite their effectiveness, we argue that such left-to-right unidirectional models are sub-optimal due to the limitations including: a) unidirectional architectures restrict the power of hidden representation in users’ behavior sequences; b) they often assume a rigidly ordered sequence which is not always practical. To address these limitations, we proposed a sequential recommendation model called BERT4Rec, which employs the deep-bidirectional self-attention to model user behavior sequences. ...
2018
- (Quadrana et al., 2018) ⇒ Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. (2018). “Sequence-Aware Recommender Systems.” In: ACM Computing Surveys (CSUR) Journal, 51(4). doi:10.1145/3190616
- QUOTE: ... In this work, we review existing works that consider information from such sequentially ordered user-item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems , and outline open challenges in the area.