Sequential Recommendation with Latent Relations based on Large Language Model

Keskeiset käsitteet

Proposing a novel framework for sequential recommendation with Latent Relation Discovery (LRD) enhances the performance of relation-aware models by capturing latent item relations.

Tiivistelmä

The content introduces a novel framework for sequential recommendation with Latent Relation Discovery (LRD) to enhance the performance of relation-aware models. The framework leverages Large Language Models (LLMs) to mine latent relations between items for recommendation. Experimental results demonstrate significant performance improvements on multiple datasets. The proposed method does not rely on manually predefined relations, allowing for the autonomous discovery of latent relations.

Introduction to Sequential Recommendation: Various methods have been proposed to predict the next item a user prefers based on their interaction history.
Challenges in Existing Methods: Traditional methods rely on implicit preferences and overlook explicit relations between items.
Proposed Framework with LRD: Introduces a novel framework with Latent Relation Discovery (LRD) to autonomously discover latent item relations.
Optimization Objective: A self-supervised learning method inspired by the Discrete-state Variational Autoencoder (DVAE) is used to predict latent relations.
Joint Learning: Joint optimization of latent relation discovery and recommendation tasks significantly improves performance.
Experimental Results: The proposed LRD-enhanced models outperform traditional and relation-aware models on multiple datasets.
Ablation Study: Removing components like Large Language Models (LLMs) and Knowledge Graph Embedding (KGE) leads to performance decline.
Latent Relation Analyses: Analysis of relation embeddings and representative item pairs demonstrate the effectiveness and reliability of discovered latent relations.

Tilastot

Recent relation-aware sequential recommendation models have achieved promising performance.
The proposed framework leverages Large Language Models (LLMs) to mine latent relations between items.
Experimental results demonstrate significant performance improvements on multiple datasets.

Lainaukset

"LRD does not rely on manually defined relations and can autonomously discover latent relations between items."
"The proposed latent relations discovery method significantly improves the performance of existing relation-aware sequential recommendation models."

Tärkeimmät oivallukset

Sequential Recommendation with Latent Relations based on Large Language Model

by Shenghao Yan... klo arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18348.pdf

Syvällisempiä Kysymyksiä

How does the proposed LRD framework compare to traditional collaborative filtering methods

The proposed Latent Relation Discovery (LRD) framework offers significant advantages over traditional collaborative filtering methods in the realm of recommendation systems. While traditional collaborative filtering methods rely on capturing implicit user preferences based on historical interactions, LRD goes a step further by explicitly incorporating item relations into the modeling of user historical sequences. By leveraging Large Language Models (LLMs) to autonomously discover latent relations between items, LRD enhances the model's ability to capture diverse preferences reflected in user interaction history. This approach allows for a more comprehensive understanding of user preferences and leads to improved recommendation performance compared to traditional collaborative filtering methods.

What are the implications of autonomously discovering latent relations for recommendation systems

The autonomous discovery of latent relations in recommendation systems has several implications for enhancing the effectiveness and efficiency of the recommendation process. By autonomously discovering latent relations, recommendation systems can uncover complex and diverse connections between items that may not be captured by predefined relations or traditional collaborative filtering methods. This leads to a more nuanced understanding of user preferences and behavior, enabling more personalized and accurate recommendations. Additionally, autonomously discovering latent relations can help address the sparsity issue often encountered in recommendation systems, as it allows for the exploration of a wider range of item relations that may not be explicitly defined. Overall, autonomously discovering latent relations can significantly improve the generalization ability and performance of recommendation systems in diverse scenarios.

How can the concept of latent relations be applied to other domains beyond sequential recommendation

The concept of latent relations can be applied to various domains beyond sequential recommendation to enhance the understanding of relationships between entities and improve recommendation systems. In e-commerce, for example, autonomously discovering latent relations can help identify hidden connections between products, leading to more accurate product recommendations and cross-selling opportunities. In healthcare, latent relations can be used to uncover hidden patterns in patient data, enabling more personalized treatment recommendations. In social networks, latent relations can help identify underlying connections between users, leading to improved friend recommendations and content personalization. Overall, the concept of latent relations has broad applicability across different domains to enhance recommendation systems and uncover valuable insights from data.