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Sequential Recommender Systems: Challenges, Progress and Prospects

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arxiv 2001.04830 v1 pith:QQBNIT5Z submitted 2019-12-28 cs.IR cs.LG

Shoujin Wang , Liang Hu , Yan Wang , Longbing Cao , Quan Z. Sheng , Mehmet Orgun This is my paper · ORCID
classification cs.IRcs.LG
keywords srssrecommenderresearchsequentialsystemsareachallengesfiltering
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The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.In this paper, we provide a systematic review on SRSs.We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.Finally, we discuss the important research directions in this vibrant area.

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Cited by 15 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation

    cs.IR 2026-08 conditional novelty 6.0 of 10

    GALLM injects item co-occurrence and item-text relations into LLM attention as learnable biases, improving sequential recommendation accuracy on four benchmarks.

  2. SeqLLM: Augmenting LLMs with Behavioral-Sequence Modeling for High-Stakes Decisions at WeChat Pay

    cs.CL 2026-08 conditional novelty 6.0 of 10

    SeqLLM injects behavioral-sequence modeling into a pretrained LLM through a discrete field-level vocabulary, a text-grounded residual projector, and task-prefixed SFT instead of continual pretraining, preserving langu...

  3. Learning from the Future: Privileged Self-Distillation for Sequential Recommendation

    cs.IR 2026-07 conditional novelty 6.0 of 10

    Privileged Self-Distillation turns future interactions into soft training targets for a causal sequential recommender via dual attention masks, gated KL distillation, and an EMA teacher.

  4. Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding

    cs.AI 2026-07 conditional novelty 6.0 of 10

    GenCDSR combines shared/domain-specific item tokenization with serial-parallel decoding, improving cross-domain sequential recommendation accuracy by ~1.5% while cutting inference latency by ~85%.

  5. Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation

    cs.IR 2025-01 conditional novelty 6.0 of 10

    IDCLRec disentangles user behaviors into interests and intents and applies intent-item contrastive learning to improve sequential recommendation accuracy beyond current baselines.

  6. InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction

    cs.IR 2024-11 conditional novelty 6.0 of 10

    A bidirectional, interleaved interaction module with a separate Cross Arch for selective summarization improves CTR prediction over unidirectional fusion baselines by small margins on public and industrial data.

  7. The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation

    cs.IR 2025-12 conditional novelty 5.0 of 10

    A dual-branch recommender that merges hash-ID and semantic-ID representations outperforms baselines while improving tail-item accuracy without losing head-item accuracy.

  8. Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation

    cs.IR 2025-01 conditional novelty 5.0 of 10

    HM4SR improves multi-modal sequential recommendation by routing item features through two mixture-of-experts layers that use timestamps and by adding category prediction and contrastive objectives.

  9. Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation

    cs.IR 2024-12 conditional novelty 5.0 of 10

    Molar combines multimodal LLM item embeddings with a contrastive post-alignment to an ID-based sequential recommender, reporting improved NDCG and Recall on three datasets.

  10. Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models

    cs.IR 2024-12 conditional novelty 5.0 of 10

    RSLLM mixes item ID embeddings from classical recommenders with text titles inside an LLM prompt and uses two-stage contrastive fine-tuning to improve sequential recommendation.

  11. Graph Structure Refinement with Energy-based Contrastive Learning

    cs.LG 2024-12 conditional novelty 5.0 of 10

    A new framework, ECL-GSR, uses energy-based contrastive learning to refine noisy graph structure and reports state-of-the-art node classification accuracy on eight benchmarks.

  12. Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation

    cs.IR 2024-11 conditional novelty 5.0 of 10

    M-GPT combines interaction-level graph convolution with multi-scale session attention and reports state-of-the-art next-purchase accuracy on three multi-behavior datasets.

  13. FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation

    cs.IR 2025-07 conditional novelty 4.0 of 10

    FindRec combines Mamba temporal encoding, RBF-kernel cross-modal alignment, and expert routing to improve multimodal sequential recommendation, reporting 1.0 to 3.3 percent relative gains over baselines, with no proof...

  14. HMamba: Hyperbolic Mamba for Sequential Recommendation

    cs.IR 2025-05 reject novelty 4.0 of 10

    HMamba is an architecture that runs Mamba's selective state space model in hyperbolic space for sequential recommendation, claiming 3-11% gains over baselines on four benchmarks.

  15. M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation

    cs.IR 2025-05 conditional novelty 4.0 of 10

    M2Rec blends Mamba sequence modeling, a learnable FFT noise filter, and LLM text embeddings through two scalar weights, reporting 3% HR@10 gains over Mamba baselines.

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