Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue

E-commerce pre-sales dialogue aims to understand and elicit user needs and preferences for the items they are seeking so as to provide appropriate recommendations. Conversational recommender systems (CRSs) learn user representation and provide accurate recommendations based on dialogue context, but rely on external knowledge. Large language models (LLMs) generate responses that mimic pre-sales dialogues after fine-tuning, but lack domain-specific knowledge for accurate recommendations. Intuitively, the strengths of LLM and CRS in E-commerce pre-sales dialogues are complementary, yet no previous work has explored this. This paper investigates the effectiveness of combining LLM and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods: CRS assisting LLM and LLM assisting CRS. We conduct extensive experiments on a real-world dataset of Ecommerce pre-sales dialogues. We analyze the impact of two collaborative approaches with two CRSs and two LLMs on four tasks of Ecommerce pre-sales dialogue. We find that collaborations between CRS and LLM can be very effective in some cases.Comment: EMNLP 2023 Finding

Design of Magnetic Bearing Control System Based on Active Disturbance Rejection Theory

At present, most of the magnetic bearing system adopts the classical proportional-integral-derivative (PID) control strategy. However, the external disturbances, system parameter perturbations, and many other uncertain disturbances result in PID controller difficult to achieve high performance. To solve this problem, a linear active disturbance rejection controller (LADRC) based on active disturbance rejection controller (ADRC) theory was designed for magnetic bearing. According to the actual prototype parameters, the simulation model was built in MATLAB/SIMULINK. The step and sinusoidal disturbances with PID and LADRC control strategies were simulated and compared. Then, the experiments of step and sinusoidal disturbances were performed. When control parameters are consistent, the experiment showed that the rotor displacement fluctuation decreased by 28.6% using the LADRC than PID control under step disturbances and decreased by around 25.8% under sinusoidal disturbances. When the rotor is running at 24,000 r/min and 27,000 r/min, the displacement of rotor is reduced by around 15% and 13.7%, respectively. Rotate the rotor with step disturbances and sinusoidal disturbances. It can also be seen that LADRC has the advantages of fast response time and good anti-interference. The experiments indicate that the LADRC has better anti-interference performance compared with PID controller

U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation

Conversational recommender systems (CRSs) aim to understand the information needs and preferences expressed in a dialogue to recommend suitable items to the user. Most of the existing conversational recommendation datasets are synthesized or simulated with crowdsourcing, which has a large gap with real-world scenarios. To bridge the gap, previous work contributes a dataset E-ConvRec, based on pre-sales dialogues between users and customer service staff in E-commerce scenarios. However, E-ConvRec only supplies coarse-grained annotations and general tasks for making recommendations in pre-sales dialogues. Different from that, we use real user needs as a clue to explore the E-commerce conversational recommendation in complex pre-sales dialogues, namely user needs-centric E-commerce conversational recommendation (UNECR). In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios. U-NEED consists of 3 types of resources: (i) 7,698 fine-grained annotated pre-sales dialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148 product knowledge tuples. To facilitate the research of UNECR, we propose 5 critical tasks: (i) pre-sales dialogue understanding (ii) user needs elicitation (iii) user needs-based recommendation (iv) pre-sales dialogue generation and (v) pre-sales dialogue evaluation. We establish baseline methods and evaluation metrics for each task. We report experimental results of 5 tasks on U-NEED. We also report results in 3 typical categories. Experimental results indicate that the challenges of UNECR in various categories are different.Comment: SIGIR23 Resource Trac