Recommendation System - Cleveland State Universitycis.csuohio.edu/~sschung/CIS660/CIS660Lecture...The recommendation system is a system for processing information, which can recommend

Recommendation System

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Introduction

Data Preprocessing

Building Model and Evaluation

Summary

Outlines

Introduction

What is recommendation system?

If you love online

shopping, you

might think of

this website.


If you love music,

you might think

of this website.

If you love to use

social media, you

might think of

Facebook or

Twitter.

The recommendation system is a system for processing information, which can recommend items that meet the requirements

for the user through certain algorithms based on the user's historical behavior data or item data. It is used to recommend

personalized news, videos, music, books, food, clothes and other items to users. In addition, it is used to recommend friends

to users in the social network such as Twitter and Facebook. The recommendation system has become an indispensable part

of many websites and mobile apps. These apps and websites rely on referral systems to increase sales, engage users, increase

user activity and attract new users.


• Objective 1: Assist users to find the items they need.

• Objective 2: Filter the information from networks.

• Objective 3: Attract users.

• Objective 4. Provide personalized services.

What is the purpose of recommendation system?

Recommendation algorithm

age

gender

Job

…

attribute

feature

…

Recommendation

list

item1

Item2

…

algorithm

item

user

What is algorithm? We

can simplify it as a

function. The function

accepts parameters

and outputs a return

value. The input

parameters have

various attributes and

characteristics of the

user and item,

including age, gender,

territory, category of

the product, release

time, and so on. After

processing by the

recommended

algorithm, it returns a

list of items sorted

according to user

preference.

• Content-based recommendation

• Collaborative filtering recommendation

user-based CF

item-based CF

model-based CF

• matrix decomposition

• Factorization Machine

• Deep learning

• Learning to rank

• Explore and exploit(Contextual bandit)

• Ensemble/Hybrid method

Popular recommendation algorithm

Content-based recommendation

If a person likes beer with sour taste, the system finds other beers with all the similar flavors and recommend it to him.

Content-based recommendations seem to be the easiest implementation that is the pretty effective. However, it is difficult to get the attributes of an item in most cases. Finding two similar products based on the attributes for recommendation is not an easy task.

Collaborative filtering recommendation(user-based)

Collaborative Filtering (CF) is a very commonalgorithm that are used in online stores. TheCF algorithm includes the user-based CF andthe item-based CF.

The user-based CF:• Analyze the current user’s rating of the item• Calculate the similarity between all user's• Select the N users that are most similar to

the current user• Recommend items that the current user has

not browsed from the N users with thehighest rating

Collaborative filtering recommendation(item-based)

The item-based CF:• Analyze each user’sbrowsing history of theitem

• Analyze he similaritybetween all items that arebrowsed

• Find N items with thehighest similarity to thecurrent item

• Recommend these N itemsto the user

matrix decomposition

There are four users ABCD and four productsWXYZ. We know some of the users’ preference forthe products which is shown in the Rating Matrix.The preference of A for X is 4.5. What is thepreference of A for W?We find two other matrices, the User Matrix andthe Item Matrix, to decompose the Rating Matrix.The number of rows of User Matrix and RatingMatrix must be the same. The number of columnsof Items Matrix and Rating Matrix must be thesame.We need to make the multiplication of User Matrixand Item Matrix is closer to the Rating Matrix. Ifthis can be done, the product matrix contains thepredicted values, which is the basic idea of matrixdecomposition.Matrix decomposition only takes advantage of thevalue of the user’s preference. The user’s attributessuch as gender and age and attributes of theproducts are not considered.

Factorization Machine

Factorization Machines (FM) is a user response prediction system that models the relevance between different entities. Each user-item interaction is then encoded into several possible matrix components. The user’s preference for the item is transferred into a formula.For example, there are three users (Tom, Jack, and Alice) and three items (movies, books, and music). It contains User Index, Item Index, User Features, and Item Features.We want to use the values of X to produce a value of Y by calculation.FM is very different from matrix decomposition. There is a product between X. For example, Tom’s X value is multiplied by Tom's age and gender. Tom’s value is also multiplied by the item’s attributes. Gender or age is also multiplied by the item, and then cross-multiplied to get our variables.

Deep learning

The deeper learning is used for developrecommendation model

• Input user ID• Add an embedded layer• Connect the outputs of two vectors

of the embedded layer• Add a fully connected layer• Add regularization• Add a fully connected layer again• Add regularization again• Add two fully connect the layers

again• Use SOFTMAX as the prediction

User embedding Item embedding

concatenate

dense

Batch normalization

dense

dense

softmax

dense

Batch normalizationdropout

dropout

predication

Deep learning—Google Play App Recommendation

This is Google Play recommendations by machine learning. The inputs are the user’s gender, age, how many apps the user has installed. The information is sent directly to the first embedded layer. The user’s device information and the user’s rating of the apps are added to the second embedding layer, which is linked to the first embedding layer. The product of the installed APP and the user’s rating is used as the final input to a neural network.

Learning to rankIf you enter a phrase into a search engine, it will feed back a lot of pages. The articles in the first page is generated by the recommendation system. These are the articles that the system thinks users would like.

This kind of recommendation is the process of sorting a bunch of items to a user when they are presented. The items that the user likes are not important, and the important ones are items that the user prefers.

If you collect user data and user-scoring data, you can use traditional search engine methods to score each item and then sort it. This is a very traditional but useful way.

Explore and exploit(Contextual bandit)

Explore and exploit sends random recommendations to users and get feedback from the users.

For example: There are 5 users with similar tastes. Give user1 and user2 two movies and see how they react to the movie.Recommend one for the first user and another for the second user. We found that the first user did not click on the movie and did not watch the movie, but the second user saw it and said that the movie is more interesting than the first movie.

Then we know that the movie recommended for the second user is good, so we can recommend other users to the movies that the second user clicked.

Ensemble/Hybrid method

• Voting

mode

average

weighted average

• Stacking

• Boosting

We have a lot of different recommendation algorithms. How do we combine these algorithm outputs?The first way is to vote. Suppose there are three recommendation systems, two of which think that one user likes the item 5 and only one thinks it is 4, then I definitely believe the two. Or taking the average, but I think the first algorithm is better, and giving it a greater weight.The second way to integrate learning is to stack. Suppose there are two kinds of recommendation algorithms. I use the result of the output of the first algorithm as part of the input or input of the second algorithm, and then train the second algorithm.The third one is promotion. We use the deviation between the output value of a recommendation algorithm and the real value to train the algorithm.

Data Preprocessing

Data preprocessing

Our topic is on

the real estate

recommendation

system

Data preprocessing

These are the features that are selected for data preprocessing.

Data preprocessing

Data preprocessing



Cosine similarity is used to compute the distance between every two items.

• Cosine similarity



• Query result

propertyId SALES_AMOU ROAD_TYPE WATER SEWER GAS ELECTRICIT GCERT1 GCERT2 RES_BLDG_C TOTAL_RES_ TOTAL_COM_ TOTAL_SQUA SiteCat1 CosineSimilarity Rank

365050 100000 PV MUN SNS Y Y 13800 65700 1 1000 0 4800 Residential 1 1

20779 100000 PV MUN SNS Y Y 17700 63400 1 1272 0 6160 Residential 0.988613 2









Building Model and Evaluation• KNN + TopN

• The selection of k value

• Cross validation

• Distance measurement

• Euclidean Distance

• Manhattan Distance

• Minkowski Distance

• Classification decision rules


• Brute Force • KD Tree • Ball Tree


• Build model


• Build model


• Query result

propertyId SALES_AMOU ROAD_TYPE WATER SEWER GAS ELECTRICIT GCERT1 GCERT2 RES_BLDG_C TOTAL_RES_ TOTAL_COM_ TOTAL_SQUA SiteCat1 KNN Rank

322514 100000 PV MUN SNS Y Y 17800 61100 0 0 4166 10000 Commercial 1










Summary

Use PCA to select features that have high weight in the future.Import user data and rating data to build better recommendation system in the future.Include more recommendation algorithm and voting to improve the results.

Summary

Build a website implement this algorithm.

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Recommendation System - Cleveland State Universitycis.csuohio.edu/~sschung/CIS660/CIS660Lecture...The recommendation system is a system for processing information, which can recommend