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

Grafting of Polyester onto Graphene for Electrically and Thermally Conductive Composites

Many reported polymer/graphene composites, even some with excellent graphene dispersion, do not possess impressive conductivity, which may be due to the failure in forming interconnected conductive paths. In this work, a kind of bio-based polyester (BE) is synthesized by polycondensation between plant-derived diols and diacids. It is then grafted onto graphene oxide (GO) via the easterification between hydroxyls of BE and carboxyls of GO. Subsequently, the grafted GO is subjected to reduce by vitamin C. Because of the presence of terminal hydroxyl group in both ends of the BE chains and multiple carboxyl groups on GO, the grafts form specific spatial interconnection structure in BE matrix. The resulting BE/graphene composites possess impressive low threshold percolation of electrical conductivity, in combination of high thermal conductivity