Research On Collaborative Filtering Algorithms For Financial Products Recommendations

With the continuous development of economy and society and the active financial market,the recommendation of financial products has become a problem that can not be ignored.As a very important financial product,stock deserve special attention.Due to the late start of Chinese stock market,there are still many shortcomings of the stock market.There are widespread questions about stock recommendation and stock picking among investors.The specific performance is that the investors' current investment theoretical is not strong,and the basic knowledge of financial investment is lacking.The investment behavior is often only a vague operation,and its behavior is not very interpretable.Even investors themselves are hard to explain the logic of their investment,the biggest reason for a large part of investors to invest is often due to their "feeling",something that is unclear.Second,some existing stock recommendation models are mainly supported by the way of stock evaluation and comments.They have quite general and vague characteristics and have certain limitations.Thirdly,when constructing the recommendation model,the features are related to each other,resulting in high complexity of the model,difficulties in calculation and storage,how to preserve the main information and without losing the accuracy of the model and reduce the model's complexity is a problem worth studying.In summary,there is an urgent need for a model that accurately portrays user portraits to serve the broad masses of investors.This model should be able to make a reasonable and clear explanation of the investor's investment behavior,allowing the investors to understand in an intuitive and common way,and also have certain advantages in the convenience of the model,because for ordinary investors.The complicated model is too difficult and inconvenient to use,and this recommendation method is greatly discounted in terms of feasibility.For the existing problems,it is considered that the collaborative filtering algorithm has great advantages and relatively mature application modes in the recommended application of commodities.Inspired by this,this paper considers the integration of collaborative filtering algorithms into the recommendation of financial products.As a common financial product,stock has a unique advantage for the application of collaborative filtering algorithms.Just look the stocks like the commodities,and various indicators of stocks as a variety of characteristics of the item,the invsetor selects the stock as the customer select the commodities,and recommends the stock to the customer as the recommended commodities to the customer.The index value of the stock is calculated as the customer's score on thecommodities.Finally,the model's high complexity caused by using multiple indicator factors to build the model,which also affects the calculation and storage efficiency of the model.In this paper,across to use the Principal Component Analysis(PCA)dimensionality reduction method preserves most of the stock information,and effectively reduces the dimension of the model without affecting the accuracy of the model,and then depend on the cosine similarity and average absolute error lead to an optimal dimension reduction dimension.The generated recommendation model which is more simple,lightweight,precise and convenient.A scientific stock financial product recommendation model was implemented.This model has a certain height in theory,and it has certain innovation in its ideas.In fact,it also has strong practicality and operability.