Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation

• URL: http://arxiv.org/abs/2411.01457v1
• Date: Sun, 03 Nov 2024 06:47:45 GMT
• Title: Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation
• Authors: Mingrui Liu, Sixiao Zhang, Cheng Long,
• Abstract summary: We propose a novel structure called Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation (FAME) We leverage sub-embeddings from each head in the last multi-head attention layer to predict the next item separately. A gating mechanism integrates recommendations from each head and dynamically determines their importance.
• Score: 25.516648802281626
• License:
• Abstract: Sequential recommendation (SR) systems excel at capturing users' dynamic preferences by leveraging their interaction histories. Most existing SR systems assign a single embedding vector to each item to represent its features, and various types of models are adopted to combine these item embeddings into a sequence representation vector to capture the user intent. However, we argue that this representation alone is insufficient to capture an item's multi-faceted nature (e.g., movie genres, starring actors). Besides, users often exhibit complex and varied preferences within these facets (e.g., liking both action and musical films in the facet of genre), which are challenging to fully represent. To address the issues above, we propose a novel structure called Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation (FAME). We leverage sub-embeddings from each head in the last multi-head attention layer to predict the next item separately. This approach captures the potential multi-faceted nature of items without increasing model complexity. A gating mechanism integrates recommendations from each head and dynamically determines their importance. Furthermore, we introduce a Mixture-of-Experts (MoE) network in each attention head to disentangle various user preferences within each facet. Each expert within the MoE focuses on a specific preference. A learnable router network is adopted to compute the importance weight for each expert and aggregate them. We conduct extensive experiments on four public sequential recommendation datasets and the results demonstrate the effectiveness of our method over existing baseline models.

Related papers

• GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation [55.769720670731516]
  GaVaMoE is a novel framework for explainable recommendation. It generates tailored explanations for specific user types and preferences. It exhibits robust performance in scenarios with sparse user-item interactions.
  arXiv  Detail & Related papers  (2024-10-15T17:59:30Z)
• Attention-based sequential recommendation system using multimodal data [8.110978727364397]
  We propose an attention-based sequential recommendation method that employs multimodal data of items such as images, texts, and categories. The experimental results obtained from the Amazon datasets show that the proposed method outperforms those of conventional sequential recommendation systems.
  arXiv  Detail & Related papers  (2024-05-28T08:41:05Z)
• Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations [50.03560306423678]
  We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems. Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
  arXiv  Detail & Related papers  (2024-01-12T15:26:40Z)
• MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation [61.45986275328629]
  We propose MISSRec, a multi-modal pre-training and transfer learning framework for sequential recommendation. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation.
  arXiv  Detail & Related papers  (2023-08-22T04:06:56Z)
• Dynamic Prototype Mask for Occluded Person Re-Identification [88.7782299372656]
  Existing methods mainly address this issue by employing body clues provided by an extra network to distinguish the visible part. We propose a novel Dynamic Prototype Mask (DPM) based on two self-evident prior knowledge. Under this condition, the occluded representation could be well aligned in a selected subspace spontaneously.
  arXiv  Detail & Related papers  (2022-07-19T03:31:13Z)
• Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation [33.97708796846252]
  We introduce a new Multi-Behavior Hypergraph-enhanced Transformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels.
  arXiv  Detail & Related papers  (2022-07-12T15:07:21Z)
• GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation [5.416421678129053]
  This paper proposes a novel sequential recommendation model called "Global Interaction Aware Multi-Interest Framework for Sequential Recommendation (GIMIRec)" The performance of GIMIRec on the Recall, NDCG and Hit Rate indicators is significantly superior to that of the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-16T09:12:33Z)
• Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [56.12499090935242]
  This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems. KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics. We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
  arXiv  Detail & Related papers  (2021-10-08T09:44:00Z)
• Sparse-Interest Network for Sequential Recommendation [78.83064567614656]
  We propose a novel textbfSparse textbfInterest textbfNEtwork (SINE) for sequential recommendation. Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool. SINE can achieve substantial improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-18T11:03:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.