RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability

Recommender systems are widely used in various online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However, these models often lack interpretability, making them less reliable and transparent for both users and developers. With the emergence of large language models (LLMs), we find that their capabilities in language expression, knowledge-aware reasoning, and instruction following are exceptionally powerful. Based on this, we propose a new model interpretation approach for recommender systems, by using LLMs as surrogate models and learn to mimic and comprehend target recommender models. Specifically, we introduce three alignment methods: behavior alignment, intention alignment, and hybrid alignment. Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training. To demonstrate the effectiveness of our methods, we conduct evaluation from two perspectives: alignment effect, and explanation generation ability on three public datasets. Experimental results indicate that our approach effectively enables LLMs to comprehend the patterns of recommendation models and generate highly credible recommendation explanations.Comment: 12 pages, 8 figures, 4 table

Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations

Recommender models excel at providing domain-specific item recommendations by leveraging extensive user behavior data. Despite their ability to act as lightweight domain experts, they struggle to perform versatile tasks such as providing explanations and engaging in conversations. On the other hand, large language models (LLMs) represent a significant step towards artificial general intelligence, showcasing remarkable capabilities in instruction comprehension, commonsense reasoning, and human interaction. However, LLMs lack the knowledge of domain-specific item catalogs and behavioral patterns, particularly in areas that diverge from general world knowledge, such as online e-commerce. Finetuning LLMs for each domain is neither economic nor efficient. In this paper, we bridge the gap between recommender models and LLMs, combining their respective strengths to create a versatile and interactive recommender system. We introduce an efficient framework called InteRecAgent, which employs LLMs as the brain and recommender models as tools. We first outline a minimal set of essential tools required to transform LLMs into InteRecAgent. We then propose an efficient workflow within InteRecAgent for task execution, incorporating key components such as a memory bus, dynamic demonstration-augmented task planning, and reflection. InteRecAgent enables traditional recommender systems, such as those ID-based matrix factorization models, to become interactive systems with a natural language interface through the integration of LLMs. Experimental results on several public datasets show that InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.Comment: 16 pages, 15 figures, 4 table

RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems

This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI provides a suite of tools, including Recommender AI Agent, Recommendation-oriented Language Models, Knowledge Plugin, RecExplainer, and Evaluator, to facilitate the integration of LLMs into recommender systems from multifaceted perspectives. The new generation of recommender systems, empowered by LLMs, are expected to be more versatile, explainable, conversational, and controllable, paving the way for more intelligent and user-centric recommendation experiences. We hope the open-source of RecAI can help accelerate evolution of new advanced recommender systems. The source code of RecAI is available at \url{https://github.com/microsoft/RecAI}.Comment: 4 pages. Webconf 2024 demo trac

Cooperative Retriever and Ranker in Deep Recommenders

Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker.Comment: 12pages, 4 figures, WWW'2

Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations

The significant progress of large language models (LLMs) provides a promising opportunity to build human-like systems for various practical applications. However, when applied to specific task domains, an LLM pre-trained on a general-purpose corpus may exhibit a deficit or inadequacy in two types of domain-specific knowledge. One is a comprehensive set of domain data that is typically large-scale and continuously evolving. The other is specific working patterns of this domain reflected in the data. The absence or inadequacy of such knowledge impacts the performance of the LLM. In this paper, we propose a general paradigm that augments LLMs with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE. This paradigm relies on a domain knowledge extractor, working in three steps: 1) preparing effective knowledge for the task; 2) selecting the knowledge for each specific sample; and 3) expressing the knowledge in an LLM-understandable way. Then, the extracted knowledge is incorporated through prompts, without any computational cost of model fine-tuning. We instantiate the general paradigm on a widespread application, i.e. recommender systems, where critical item attributes and collaborative filtering signals are incorporated. Experimental results demonstrate that DOKE can substantially improve the performance of LLMs in specific domains

HULL SHAPE OPTIMIZATION OF SMALL UNDERWATER VEHICLE BASED ON KRIGING-BASED RESPONSE SURFACE METHOD AND MULTI-OBJECTIVE OPTIMIZATION ALGORITHM

Small underwater vehicles have unique advantages in ocean exploration. The resistance and volume of a vehicle are key factors affecting its operation time underwater. This paper aims to develop an effective method to obtain the optimal hull shape of a small underwater vehicle using Kriging-based response surface method (RSM) and multi-objective optimization algorithm. Firstly, the hydrodynamic performance of a small underwater vehicle is numerically investigated using computational fluid dynamics (CFD) method and the value range of related design variables is determined. The mesh convergence is verified to ensure the accuracy of the calculation results. Then, by means of the Latin hypercube sampling (LHS) design of simulation, the Kriging-based RSM model is developed according to the relation between each design variable of the vehicle and the output parameters applied to the vehicle. Based on the Kriging-based RSM model, the optimal hull shape of the vehicle is determined by using Screening and MOGA. As results, the vehicle resistance reduces and volume increases obviously

The Higgs boson inclusive decay channels H→bbˉH \to b\bar{b} and H→ggH \to gg up to four-loop level

The principle of maximum conformality (PMC) has been suggested to eliminate the renormalization scheme and renormalization scale uncertainties, which are unavoidable for the conventional scale setting and are usually important errors for theoretical estimations. In this paper, by applying PMC scale setting, we analyze two important inclusive Standard Model Higgs decay channels, $H\rightarrow b\bar{b}$ and $H\rightarrow gg$, up to four-loop and three-loop levels accordingly. After PMC scale setting, it is found that the conventional scale uncertainty for these two channels can be eliminated to a high degree. There is small residual initial scale dependence for the Higgs decay widths due to unknown higher-order $\{\beta_i\}$-terms. Up to four-loop level, we obtain $\Gamma(H\rightarrow b\bar{b}) = 2.389\pm0.073 \pm0.041$ MeV and up to three-loop level, we obtain $\Gamma(H\rightarrow gg) = 0.373\pm0.030$ MeV, where the first error is caused by varying $M_H=126\pm4$ GeV and the second error for $H\to b\bar{b}$ is caused by varying the $\overline{\rm MS}$-running mass $m_b(m_b)=4.18\pm0.03$ GeV. Taking $H\to b\bar{b}$ as an example, we present a comparison of three BLM-based scale setting approaches, e.g. the PMC-I approach based on the PMC-BLM correspondence, the $R_\delta$-scheme and the seBLM approach, all of which are designed to provide effective ways to identify non-conformal $\{\beta_i\}$-series at each perturbative order. At four-loop level, all those approaches lead to good pQCD convergence, they have almost the same pQCD series, and their predictions are almost independent on the initial renormalization scale. In this sense, those approaches are equivalent to each other.Comment: 14 pages, 7 figures. References updated and discussions improved. To be published in Eur.Phys.J.