Legal Supervision Mechanism of Recommendation Algorithm Based on Intelligent Data Recognition

Abstract

While considering the broad development prospects of intelligent investment advisers in the future, we must also be aware of the legal supervision issues that intelligent investment advisers bring with them. Simultaneously, big data, artificial intelligence, blockchain, and other technologies have advanced at a breakneck pace during this time. Many new technologies have been used in the financial sector. In the financial field, there is a trend toward gradual integration of finance and technology. The transformation of finance from concept to actual service has been realized thanks to emerging technologies. This combination of finance and technology is known as “financial technology.” The legal supervision mechanism of a recommendation algorithm based on intelligent data recognition is investigated in this paper. On the one hand, it raises the bar for legal knowledge required by the legal supervision mechanism of recommendation algorithm, but on the other hand, it may reduce service efficiency and quality. As a result, using the understanding method of users’ consulting intention while taking into account users’ legal knowledge level, social attributes, and emotional status, it is a feasible and effective way to provide personalized and diverse legal consulting services to the general public.

A Self-Adaptive Recommendation Method for Online Ideological and Political Teaching Resources Based on Deep Reinforcement Learning

“…e fuzzy adaptive scheduling scheme is implemented, and the big data information fusion model of online ideological as well as the political teaching resources adaptive recommendation is constructed. According to the data distribution characteristics of online ideological as well as the political teaching resources adaptive recommendation, the personalized recommendation algorithm is designed [9].…”

LawRec: Automatic Recommendation of Legal Provisions Based on Legal Text Analysis

“…Experimental Dataset. In the Fayan Cup dataset, an experiment was conducted on the legal article recommendation task [24]. In order to achieve a relatively balanced dataset, this paper deleted some low-frequency legal articles Computational Intelligence and Neuroscience in the Fayan Cup dataset and deleted some invalid samples and stop words, and finally, the training set selected in this paper is 800,000, and the validation set and test set are each 50,000. e number of law labels selected in this paper is about 1.1 million.…”

Predicting Stock Prices Using the Random Forest Classifier

“…the adaptive dynamic approach was used, which includes a dynamic and automatic selection of updated data in the market, and the nonadaptive standard approach was used to create recommendations for investors during the future stages. He also followed the system and relied on sliding windows in order to develop trading rules, get different samples of training and get an adaptive strategy,the GE algorithm was used as the algorithm is based on an improvement of the standard approach as it contains a hysteresis component that limits over-trading while maintaining the ability to change the active model to deal with The results were that making investing rules using syntactic development often entailed getting a single rule based on the training period.It is essential to nd alternatives that provide a balance between exibility and transaction expense.The importance of limiting over-trading, as there is an inverse relationship between the number of buy and sell orders and performance it,suggestion in [22].…”