One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems

The purpose of sequential recommendation is to utilize the interaction history of a user and predict the next item that the user is most likely to interact with. While data sparsity and cold start are two challenges that most recommender systems are still facing, many efforts are devoted to utilizing data from other domains, called cross-domain methods. However, general cross-domain methods explore the relationship between two domains by designing complex model architecture, making it difficult to scale to multiple domains and utilize more data. Moreover, existing recommendation systems use IDs to represent item, which carry less transferable signals in cross-domain scenarios, and user cross-domain behaviors are also sparse, making it challenging to learn item relationship from different domains. These problems hinder the application of multi-domain methods to sequential recommendation. Recently, large language models (LLMs) exhibit outstanding performance in world knowledge learning from text corpora and general-purpose question answering. Inspired by these successes, we propose a simple but effective framework for domain-agnostic recommendation by exploiting the pre-trained LLMs (namely LLM-Rec). We mix the user's behavior across different domains, and then concatenate the title information of these items into a sentence and model the user's behaviors with a pre-trained language model. We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems. Furthermore, we are curious about whether the latest technical advances in nature language processing (NLP) can transfer to the recommendation scenarios.Comment: 10 pages, 7 figures, 6 table

Design and Experimental Research on Soil Covering Device with Linkage and Differential Adjustment of Potato Planter

When the potato planter works on sloping field, it will cause problems such as poor film mulching quality due to the difference in volume of soil covering both sides of the discs and the inconvenient adjustment of the soil covering disc. The soil covering device with linkage and differential adjustment was designed to improve the mulching quality. The main research content includes explaining the structure and principle of the soil covering device and analyzing the structural parameters of the adjustment mechanism. The field experiment was completed to verify the performance of soil covering device, which takes the stability coefficient and uniformity coefficient of the volume of covering soil as factors. The result shows the following: (1) The volume of covering soil changes exponentially with the angle of the disc through data fitting, which can standardize the angle of covering disc; and (2) when the angle of disc is 30° and 60°, respectively, the uniformity coefficient of volume of covering soil is lower than 1.4, which has premium soil covering quality. When the angles of the discs on both sides differ greatly, the stability coefficient of volume of covering soil is 0.41, which can meet the requirements of the mulching quality of potato planter. This research provides the technical support for high-quality potato planting

Effect of Performance of Soil Cultivator with Different Surface Textures of Shovel Wing

Cultivating the soil is a necessary measure to ensure the growth of potatoes, and it has a significant impact on potato yield. In this study, a soil cultivator with a textured shovel wing was designed to address the problem that soil cultivators have poor working performance. Based on a combination of discrete element simulation and a digital soil trench verification test, the effects of the structure parameters of the surface textures on the traction resistance and soil fragmentation rate of the soil cultivator with a textured shovel wing were studied. These parameters were optimized to provide a basis for the design of the soil cultivator. The main research results of this paper are as follows. (1) The factors influencing the traction resistance of the soil cultivator were as follows: blade penetration angle > convex hull distance > convex hull diameter. The convex hull diameter was the main factor affecting the soil fragmentation rate. The traction resistance of the soil cultivator with a textured wing was reduced by 9.49%, and the soil fragmentation rate was increased by 10.67%, showing that the quality of soil cultivation was significantly improved. (2) The best parameters for the texture structure of the shovel wing were a blade penetration angle of 26°, a convex hull diameter of 34.4 mm, and a convex hull distance of 28.5 mm. (3) The relative errors between the simulation and the soil trench test for the traction resistance and the soil fragmentation rate were 2.60% and 13.97%, respectively. This study can provide technical support for the design of soil cultivators and is of great significance in improving the quality of soil cultivators