With the continuous development of network technology and the ever-expanding scale of e-commerce, the number and variety of goods grow rapidly and users need to spend a lot of time to find the goods they want to buy. This is information overload. To solve this problem, the recommendation system came into being.
The recommendation system is a subset of the Information Filtering System, which can be used in a range of areas such as movies, music, e-commerce, and Feed stream recommendations. The recommendation system discovers the user’s personalized needs and interests by analyzing and mining user behaviors and recommends information or products that may be of interest to the user. Unlike search engines, recommendation systems do not require users to accurately describe their needs but model their historical behavior to proactively provide information that meets user interests and needs.
In this article we use PaddlePaddle, a deep learning platform from Baidu, to build a model and combine Milvus, a vector similarity search engine, to build a personalized recommendation system that can quickly and accurately provide users with information that might be of interest to them.
Data Preparation
We take MovieLens Million Dataset (ml-1m) [1] as an example. The ml-1m dataset contains 1,000,000 reviews of 4,000 movies by 6,000 users, collected by the GroupLens Research lab. The original data includes feature data of the movie, user feature, and user rating of the movie, you can refer to ml-1m-README [2].
ml-1m dataset includes 3 .dat articles: movies.dat、users.dat and ratings.dat.movies.dat includes movie’s features, see example below:
MovieID::Title::Genres
Credit: Data Science Central By: Kate Shao
