1. Recommendation System
Liu,Quanyin
Wang,Yifan

2. Why Recommendation System Important?
We have now entered a data explosion era.
It is hard for a person to find useful information from vast amounts of data.
In many cases, users don't fully realize their own needs, or their demand is difficult to express in words.
With the development of Web 2.0, we have now entered a data explosion era, Web has become a platform for data sharing, so, let people in the vast amounts of data to find the information they need will become more and more difficult. Although we have search engines to quickly find our target information when we are relatively clear about our needs. But in many case, people will find themselves trapped in a huge amount of commodity information surrounded. So they cannot fully realize their own needs. Or sometimes , their demand is difficult to express in simple keywords.

3. Definition of Recommendation System
Recommendation system is an application of information filtering. It studies the user‘s interests and preferences, and imply some rules to find the user’s personalized needs and actively and efficiently recommend information and content to users.
Definition of Recommendation System
Under these circumstances, a new engine called recommendation system has been used. Recommendation system is an application of information filtering. It studies the user's interests and preferences, and imply some rules to find the user's personalized needs and actively and efficiently recommend information and content to users.

4. Applications of Recommendation System
Tons of websites:
Youtube, Spotify, Netflix, Google, Ebay...
the recommendation system has achieved great success in multiple e-commerce websites.

5. For example, when user using Taobao
For example, when user using Taobao.com, his browsing records, shopping cart or purchase records will be collected. And the recommendation system could capture these data which contains the potential demand and consumption habits and obtain user evaluation matrix. And use related recommendation algorithm to form personalized recommendation for users.

6. Principle behind Recommendation System
There are many different theories and algorithms behind recommendation system at current stage.
Theories and algorithms are not independent.
They are cooperating with each other to achieve the best results.
When it comes to recommender systems, of course, the core recommendation algorithm is indispensable. There are many different theories and algorithms behind recommendation system at current stage. In this paper, we will introduce some basic algorithm.

7. Basic Idea: UserStatistic-based Recommendation
This is the simplest recommendation algorithm, it is simply based on the basic information of users in the system to find the relevance of the user, and then recommend other items which similar users like to the current user.
Basic Idea: UserStatistic-based Recommendation
The first one is User Statistic-Based recommendation
This is the simplest recommendation algorithm, it is simply based on the basic information of users in the system to find the relevance of the user, and then recommend other items which similar users like to the current user.
For example, as shown in the graph, User A and C are both female at twenties. In the recommendation engine, they can be called "neighbors" because their similarity; finally, based on the "neighbor" user group preferences, A’s favorite goods will be recommended to C.
It does not require the current user preferences for historical data, so new users could also use.
The algorithm is relatively rough, the effect is very difficult to be satisfied, only suitable for simple recommendation.

8. Basic Idea: Content-based Recommendation
The most widely used recommendation mechanism in early recommendation engine.
Its core idea is based on the metadata of recommended items or content, discover the relevance of an article or content, and recommend similar item to user
Basic Idea: Content-based Recommendation
The second algorithm is the most widely used recommendation mechanism in early recommendation engine. Its core idea is based on the metadata of recommended items or content, discover the relevance of an article or content. and recommend similar item to user based on the user's previous preference records. In this graph, the system first builds the attributes of the commodity books. Through the similarity calculation, it found books A and C are similar books because they are both youth literature. When they find user A like Book A, they may recommend book C to A since A is likely to be interested in book C.
The results are not always accurate.
Cannot find new interesting products for users.

9. Basic Idea: Association Rule-based Recommendation
Associated product is not necessarily complementary product:
The findings were that men between years in age, shopping between 5pm and 7pm on Fridays, who purchased diapers on behalf of their wives were most likely to also have beer in their carts. This motivated the grocery store to move the beer isle closer to the diaper isle and wiz-boom-bang, instant 35% increase in sales of both.
Association Rule-based Recommendation is based on these kind of association rules, which takes the purchased goods as the rule head, and the rule body as the recommended object. The association rule is to count how many people will buy B when they have bought A in the transaction database.
The advantages of this algorithm are that it has ability to find new interest points and don't require domain knowledge.

10. More Advanced Algorithms: Collaborative filtering algorithm
Through the continuous interaction between users and websites, recommendation system keep learning on user's real interests, hence gradually filtering out non-focus options, and finally recommend the products that users are really interested in.
Adding to shopping cart,
Click ‘fav’ button or
‘No,I’m not interested in’.
The idea of collaborative filtering is like this: to recommend a commodity to a user which he is really interested in, the first thing is to find other users who have similar interests with this user. And then recommend the content that similar users are interested in to this user.
Collaborative filtering algorithm can filter information that is difficult for machine to automatic content analysis, such as artwork, music, etc.
Sharing the experience of other people avoids the incomplete and inaccurate content analysis, and can filter some complex and difficult concepts (such as information quality, personal taste).

11. Obtaining Feedbacks from Users: Also a part of Collaborative filtering
Explicit feedback:
User show their interests in a proactive way:
Leaving comments，
Giving ratings,
Adding to shopping cart or wish list.
Implicit feedback：
Staying time of a page,
Users’ click behavior
Explicit user feedback: This is the user's natural browsing of the web site or the use of Web sites to explicitly provide feedback information, such as user ratings of items, or comments on objects.
Implicit user feedback: This is the data generated by the user using the site, implicitly reflects the user's preferences for items, such as the users’ staying time on a page. And click behavior

12. Challenges

13. Cold-start problem
New product does not have any user rating, so recommendation system will not recommend it to any users.
New users don't have historical purchase records, and recommendation system has nothing to recommend to them.
The solution is to pick several popular products from a very wide range of coverage; or directly use users’ registration information or behavior data of user which imported from other websites to recommend commodities to customers
Cold start problems occur when new users or new products are just added to the system.
The cold start problem mainly includes 3 types: new user problem, new product problem and new system problem. In this case, the recommendation system cannot recommend. For new users, there is almost no information related to the user in the system; for the new product, there is almost no user rating record for it. Therefore, the traditional collaborative filtering method is difficult to recommend for new users and new products. For the new system, because the system has almost no information about users and goods, the recommendation system cannot find the relevant recommendation model.

14. Robustness of recommendation algorithm
The recommendation system can affect the user's buying behavior, bring economic benefits, so more and more malicious users trying to influence the behavior of the system through the recommendation to control the recommendation system in order to improve the sales of goods, damaged rival interests, or even destroy the system so that it cannot produce effective recommendation.
In response to these attacks, there are usually two ways to deal with them, and they are two significant problems in the vulnerability of recommendation systems. One is attack detection - detection of attacks in the system, and then eliminate or reduce the impact of these attacks on the recommendation. The other is to design a more robust algorithm. Up to now, the research on robustness of recommender systems is still few, lacking systematic analysis.

15. Thanks for listening