just thinking of film recommendation system

Take a glance at DOUBAN's films recommendation system
The course ’social media analytics’ is almost getting to the end. The most significant change for me is that it helped build up a conception of classification for me and also figured out how important it is. many tools we use to analyze information make a specific emphasis on classification. We need to classify the users groups, users emotional trend, users’ preference and so on to establish a integrated platform. As is known to all,  there are few popular social media apps in china such as weibo, qq zone, renren, but  little of them are derived  from our own ideas, we are used to plagiarize one’s creation even overseas and most of them happened to be also popular in China. It seems that social media plays such a significant role in our life, not only for that we are depended on wechat everyday, but also for it a necessary access for economic commerce to gain information from customers. And in last class we also learnt the movie recommend system which also implies a very important application of social media, most of us have the experience of searching for some certain of films or recorded our watching history down to record our preference, and it all provided plenty of information to form a smart recommend system to which depend on a well-designed movie grading system, knowing about the principle of apps we used frequently did make us a firm fundament to design a similar system in our project.

and we happened to learn the recommendation system in class

some basic idea of recommendation system

Basic Idea of Recommender Systems
– Use computer algorithms to filter information for the users
– Compare user preferences and item characteristics in a large scale 

In collaborative filtering, we usually have to work on the user-item matrix as shown above,the horizon and vertical axes shown items and users respectively, and the add-up matrix shows the relationship of the two, to measure how the users are into one film, we generate a specific algorithm to score the preference of one particular user.
step much more further, we extract some basic elements of one movie such as fantasy and authentic,romantic and unromantic, and formed two matrix to illustrate to what extend one movie can express these elements,and to what extent a user prefer to such specific elements as follows:

and what's the assumption? it shows a set of latent features exist and the user’s rating of an item is a linear combination of these latent features. thus we generate a simple fundament of recommendation system. and that's also a vital part in project for what we also decided to look into the recommendation system.