6 research outputs found
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