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Recommendation system (RS) plays significant roles in matching users information needs for Internet applications, and it usually utilizes the vanilla neural network as the backbone to handle embedding details. Recently, the large language model (LLM) has exhibited emergent abilities and achieved great breakthroughs both in the CV and NLP communities. Thus, it is logical to incorporate RS with LLM better, which has become an emerging research direction. Although some existing works have made their contributions to this issue, they mainly consider the single key situation (e.g. historical interactions), especially in sequential recommendation. The situation of multiple key-value data is simply neglected. This significant scenario is mainstream in real practical applications, where the information of users (e.g. age, occupation, etc) and items (e.g. title, category, etc) has more than one key. Therefore, we aim to implement sequential recommendations based on multiple key-value data by incorporating RS with LLM. In particular, we instruct tuning a prevalent open-source LLM (Llama 7B) in order to inject domain knowledge of RS into the pre-trained LLM. Since we adopt multiple key-value strategies, LLM is hard to learn well among these keys. Thus the general and innovative shuffle and mask strategies, as an innovative manner of data argument, are designed. To demonstrate the effectiveness of our approach, extensive experiments are conducted on the popular and suitable dataset MovieLens which contains multiple keys-value. The experimental results demonstrate that our approach can nicely and effectively complete this challenging issue., Comment: Accepted by CIKM2023 workshop at GenRec'23

To develop highly sensitive, stable and repeatable surface-enhanced Raman scattering (SERS) substrates is crucial for analytical detection, which is a challenge for traditional metallic structures. Herein, by taking advantage of the high surface activity of 1T' transition metal telluride, we have fabricated high-density gold nanoparticles (AuNPs) that are spontaneously in-situ prepared on the 1T' MoTe2 atomic layers via a facile method, forming a plasmonic-2D material hybrid SERS substrate. This AuNP formation is unique to the 1T' phase, which is repressed in 2H MoTe2 with less surface activity. The hybrid structure generates coupling effects of electromagnetic and chemical enhancements, as well as excellent molecule adsorption, leading to the ultrasensitive (4*10^-17 M) and reproducible detection. Additionally, the immense fluorescence and photobleaching phenomena are mostly avoided. Flexible SERS tapes have been demonstrated in practical applications. Our approach facilitates the ultrasensitive SERS detection by a facile method, as well as the better mechanistic understanding of SERS beyond plasmonic effects.