Unified Semantic and ID Representation Learning for Deep Recommenders

TL;DR

This paper introduces a unified framework combining semantic and ID tokens for improved large-scale recommender systems, addressing issues of redundancy and cold-start performance, and demonstrating significant empirical gains.

Contribution

The paper proposes a novel unified learning framework that leverages both semantic and ID tokens, integrating cosine similarity and Euclidean distance for enhanced representation learning in recommenders.

Findings

Outperforms state-of-the-art baselines by 6-17%

Reduces token size by over 80%

Effectively combines semantic and ID tokens for better generalization

Abstract

Effective recommendation is crucial for large-scale online platforms. Traditional recommendation systems primarily rely on ID tokens to uniquely identify items, which can effectively capture specific item relationships but suffer from issues such as redundancy and poor performance in cold-start scenarios. Recent approaches have explored using semantic tokens as an alternative, yet they face challenges, including item duplication and inconsistent performance gains, leaving the potential advantages of semantic tokens inadequately examined. To address these limitations, we propose a Unified Semantic and ID Representation Learning framework that leverages the complementary strengths of both token types. In our framework, ID tokens capture unique item attributes, while semantic tokens represent shared, transferable characteristics. Additionally, we analyze the role of cosine similarity and Euclidean distance in embedding search, revealing that cosine similarity is more effective in decoupling accumulated embeddings, while Euclidean distance excels in distinguishing unique items. Our framework integrates cosine similarity in earlier layers and Euclidean distance in the final layer to optimize representation learning. Experiments on three benchmark datasets show that our method significantly outperforms state-of-the-art baselines, with improvements ranging from 6\% to 17\% and a reduction in token size by over 80%. These results demonstrate the effectiveness of combining ID and semantic tokenization to enhance the generalization ability of recommender systems.