LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding

TL;DR

This paper introduces LARR, a novel recommendation system leveraging large language models for real-time scene understanding, improving efficiency and personalization in CTR prediction by integrating semantic insights without processing entire scene texts.

Contribution

The paper proposes a new LLM-based framework for real-time scene recommendation that efficiently incorporates semantic understanding and domain-specific knowledge into CTR prediction.

Findings

Enhanced CTR prediction accuracy demonstrated.

Efficient real-time scene understanding achieved.

Improved personalization in recommendation tasks.

Abstract

Click-Through Rate (CTR) prediction is crucial for Recommendation System(RS), aiming to provide personalized recommendation services for users in many aspects such as food delivery, e-commerce and so on. However, traditional RS relies on collaborative signals, which lacks semantic understanding to real-time scenes. We also noticed that a major challenge in utilizing Large Language Models (LLMs) for practical recommendation purposes is their efficiency in dealing with long text input. To break through the problems above, we propose Large Language Model Aided Real-time Scene Recommendation(LARR), adopt LLMs for semantic understanding, utilizing real-time scene information in RS without requiring LLM to process the entire real-time scene text directly, thereby enhancing the efficiency of LLM-based CTR modeling. Specifically, recommendation domain-specific knowledge is injected into LLM and then RS employs an aggregation encoder to build real-time scene information from separate LLM's outputs. Firstly, a LLM is continual pretrained on corpus built from recommendation data with the aid of special tokens. Subsequently, the LLM is fine-tuned via contrastive learning on three kinds of sample construction strategies. Through this step, LLM is transformed into a text embedding model. Finally, LLM's separate outputs for different scene features are aggregated by an encoder, aligning to collaborative signals in RS, enhancing the performance of recommendation model.