Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation (2311.09049v4)

Abstract: Recently, LLMs have shown great potential in recommender systems, either improving existing recommendation models or serving as the backbone. However, there exists a large semantic gap between LLMs and recommender systems, since items to be recommended are often indexed by discrete identifiers (item ID) out of the LLM's vocabulary. In essence, LLMs capture language semantics while recommender systems imply collaborative semantics, making it difficult to sufficiently leverage the model capacity of LLMs for recommendation. To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems. Our approach can directly generate items from the entire item set for recommendation, without relying on candidate items. Specifically, we make two major contributions in our approach. For item indexing, we design a learning-based vector quantization method with uniform semantic mapping, which can assign meaningful and non-conflicting IDs (called item indices) for items. For alignment tuning, we propose a series of specially designed tuning tasks to enhance the integration of collaborative semantics in LLMs. Our fine-tuning tasks enforce LLMs to deeply integrate language and collaborative semantics (characterized by the learned item indices), so as to achieve an effective adaptation to recommender systems. Extensive experiments demonstrate the effectiveness of our method, showing that our approach can outperform a number of competitive baselines including traditional recommenders and existing LLM-based recommenders. Our code is available at https://github.com/RUCAIBox/LC-Rec/.

Analyzing LC-Rec: Integrating Language and Collaborative Semantics in LLMs for Recommender Systems

The paper introduces LC-Rec, an advanced model for enhancing recommendation systems using LLMs. This model focuses on addressing the semantic gap between language semantics inherent to LLMs and collaborative semantics integral to traditional recommender systems. LC-Rec achieves this integration through a strategic approach combining item indexing and alignment tuning.

Item Indexing with Vector Quantization

A critical innovation of LC-Rec is its vector quantization (VQ) based item indexing mechanism. Traditional recommendation systems rely heavily on discrete item IDs which do not efficiently map onto the semantic space of LLMs. LC-Rec's indexing overcomes this challenge by using a tree-structured vector quantization system. This system maps items to unique and meaningful indices derived from text embeddings created by LLMs. In resolving potential indexing conflicts, a uniform semantic mapping technique is employed. This approach not only reduces conflicts but also aligns item representation more closely with language semantics, effectively expanding the expressiveness of the LLM to accommodate collaborative semantics.

Alignment Tuning for Enhanced Semantic Integration

Beyond indexing, LC-Rec's robustness arises from its comprehensive suite of alignment tuning tasks aimed at integrating language and collaborative semantics. The paper details various alignment strategies:

1. Sequential Item Prediction: This task sets the stage for LLM adaptation by framing item prediction as a task similar to language generation. By reformulating user-item interaction histories into index sequences, LLMs are prompted to predict subsequent interactions.
2. Explicit Index-Language Alignment: To strengthen the connection between item indices and their language-based descriptions, the model involves tasks where the LLM is instructed to generate indices from item text and vice versa. This mutual prediction fosters a deeper semantic understanding between item indices and their language representations.
3. Implicit Recommendation-oriented Alignment: The paper introduces comprehensive alignment tasks to harness the LLM's language capabilities further. These include asymmetric item prediction, item prediction based on inferred user intentions, and personalized preference inference. Each task is designed to push the bounds of the model's ability to unify index-based semantics with language-based expectations.

Experimental Validation

LC-Rec was extensively tested on Amazon datasets spanning domains like musical instruments, arts, and video games. Compared to state-of-the-art models, LC-Rec demonstrated superior performance, significantly improving hit rates and NDCG scores across all datasets. Particularly, the model achieved an average improvement of 25.5% in full ranking scenarios, indicating its robust capacity to handle complete recommendation tasks without relying solely on pre-selected candidate sets.

Implications and Future Work

LC-Rec represents a significant advancement in leveraging LLMs for recommendation tasks, showcasing how language and collaborative semantics can be integrated. The model’s approach suggests a development path for LLMs to move beyond traditional language applications into more nuanced recommender system roles, retaining their generalization abilities while adapting to specific domain needs.

Future refinements may explore strengthening the index learning process to accommodate more dynamic datasets or incorporating additional types of semantic information. Additionally, expanding the model to support multi-turn interactions or exploring domain neutrality while retaining semantic integration could pave the way for more versatile recommendation systems driven by LLMs.

Through these contributions, LC-Rec not only enhances recommendation system performance but also lays the groundwork for further exploration into the beneficial cross-domain applications of LLMs, potentially influencing a swath of AI-driven personalization and interaction tasks moving forward.