XR ec: Large Language Models for Explainable Recommendation (2024)

Abstract: Recommender systems help users navigate information overload by providing personalized recommendations aligned with their preferences. Collaborative Filtering (CF) is a widely adopted approach, but while advanced techniques like graph neural networks (GNNs) and self-supervised learning (SSL) have enhanced CF models for better user representations, they often lack the ability to provide explanations for the recommended items. Explainable recommendations aim to address this gap by offering transparency and insights into the recommendation decision-making process, enhancing users’ understanding. This work leverages the language capabilities of Large Language Models (LLMs) to push the boundaries of explainable recommender systems. We introduce a model-agnostic framework called XRec, which enables LLMs to provide comprehensive explanations for user behaviors in recommender systems. By integrating collaborative signals and designing a lightweight collaborative adaptor, the framework empowers LLMs to understand complex patterns in user-item interactions and gain a deeper understanding of user preferences. Our extensive experiments demonstrate the effectiveness of XRec, showcasing its ability to generate comprehensive and meaningful explanations that outperform baseline approaches in explainable recommender systems.

## introduction with the overwhelming abundance of content and products available in online platforms, users frequently encounter the daunting challenge of information overload. in response, recommender systems emerge as indispensable tools that aim to alleviate this burden. these systems effectively filter through the vast array of options and present users with tailored recommendations that are both relevant and personalized, aligning with their unique preferences and interests @xcite . among the diverse range of recommendation techniques available, collaborative filtering (cf) framework emerges as a prominent and widely embraced approach within the field of recommender systems. cf operates on the fundamental premise that users who have demonstrated similar preferences in the past, such as common item ratings or similar purchase histories, are likely to exhibit comparable preferences when it comes to future recommendations @xcite . in recent years, the field of collaborative filtering algorithms has undergone a remarkable revolution with the emergence of deep learning techniques. this transformative wave has brought about the integration of diverse neural network architectures, including attention mechanisms @xcite , graph neural networks (gnns) @xcite , and self-supervised learning (ssl) @xcite . notably, the incorporation of gnns in collaborative filtering models has yielded significant advancements by effectively capturing complex relational information and enhancing recommendation performance while preserving highorder collaborative dependencies. moreover, selfsupervised recommender systems have emerged as a promising solution to address the challenge of data sparsity. these systems leverage selfsupervised learning signals to augment the available data, aiming to enhance recommendations. while existing collaborative filtering models excel at providing accurate recommendation results, there remains a critical aspect that has not received adequate attention: understanding the underlying reasons behind observed user-item interactions. explainable recommendations aim to address this gap by providing transparency to users, offering insights into the decision-making process behind recommendations. this not only enhances users' understanding of their own preferences, but also fosters trust in recommendation algorithms. several research studies have dedicated their focus to generating explanations for user-item interactions. notably, att2seq @xcite and nrt @xcite employ attention mechanisms and recurrent neural networks (rnns) to generate textual explanations. recent advancements have further explored the utilization of transformer @xcite and gpt2 @xcite ) for text generation, providing valuable insights into recommendation results. however, these approaches face a common challenge arising from the limited availability of explanation data, which hinders their ability to generate high-quality explanations. it is also important to emphasize that id-based methods heavily rely on id embeddings, resulting in limited generalization capabilities and difficulties when adapting to unseen users and items in a zeroshot recommendation scenario. presented work. in light of the recent advancements in large language models (llms), our primary objective is to push the boundaries of explainable recommender systems by harnessing the exceptional language capabilities of llms. to this end, we introduce model-agnostic xrec, a groundbreaking collaborative instruction-tuning framework that enables large language models to provide comprehensive explanations for user behaviors within recommender systems. within our xrec framework, we equip llms with the unique ability to comprehend the intricate patterns of useritem interactions through the integration of collaborative signals via a collaborative instruction-tuning paradigm. in order to bridge the representation space of collaborative relationships and the language semantic space, we design a lightweight collaborative adaptor that incorporates behavior-aware collaborative signals into the llms, facilitating a deeper understanding of user preferences. we conducted a series of thorough experiments to validate the effectiveness and superior performance of our proposed framework, xrec, in generating comprehensive and meaningful explanations within recommender systems. in addition to that, we conducted ablation studies and investigated the robustness of our model, providing further evidence of its effectiveness. ## methodology in this section, we provide a comprehensive overview of our xrec, which is specifically designed to generate explanations for user-item interactions. the goal of our model is to uncover the underlying reasons behind these interactions and shed light on the factors influencing user behavior. by unifying graph collaborative filtering and large language models, our xrec aims to provide insightful explanations which help users understand why certain interactions occur and enhance the transparency of the recommendation process. ## conclusion this work presents a novel framework, xrec, that seamlessly integrates the graph-based collaborative filtering paradigm with the capabilities of large language models (llms) to generate comprehensive and insightful explanations for recommendation outputs. by leveraging the inherent collaborative relationships encoded within the user-item interaction graph, xrec is able to effectively capture the high-order dependencies that underlie user preferences and item associations. xrec introduces a specialized collaborative information adaptor, which serves as the critical bridge for establishing a strong connection between the collaborative signals and the rich textual semantics encoded within the llm. through extensive experiments, the study's findings underscore the significant advantages of the xrec framework. not only does it enhance the explainability of the recommendation process, but it also ensures robustness, particularly in challenging zero-shot scenarios where the framework demonstrates strong generalization capabilities across unseen users and items.