夏ゼミプレゼン 4xp

http://umekoumeda.net/Summer Seminar 2008 @Susukakedai

Badrul Sarwar, ”Item-Based Collaborative

Filtering Recommendation Algorithms”,

WWW 2001

Deguchi Lab.

Takashi UMEDA

Mail: umeda07[at]cs.dis.titech.ac.jp

Web: http://umekoumeda.net/

mailto:[email protected]


Outline…

• Introduction

• Item-Based CF

• Experimental Procedure

• Experimental Result

• Conclusions


INTRODUCTION

Chap.1


1-1. My Research Domain

• Evaluating recommendation Algorithms by ABM

– Recommendation:

• Rule Based Approach

• Contents Based Approach

• Collaborative Filtering(CF)

• Bayesian Network

– Why CF?

• It’s mainly used in many websites

– Why ABM?

• To use ABM, Algorithms are optimized according to the

market environment


1-2. What’s CF? (1/2)

• Have you used Amazon.com ?


1-3. What’s CF 2/2

Recommendation

Collaborative Filtering Algorithms(CF) is commonly

used in EC WebSite.


1-4. What’s CF 3/3

Book List

Book List

Prof. Kizima

Prof. Deguchi

CF will

recommend

Prof Deguchi

Follow book,

Based on people

that are similar

with him

They have same books

↓

They have similar

preference


1-5. Contribution of this paper

• Problem of the Basic CF Algorithms

– Basic CF : Nearest Neighbors

– Scalability(Performance)

• High Scalability : In many users, a system recommend for them quickly

– Accuracy(Quality)

• High Accuracy : if the data were sparse, a system recommend the item that a user may like

• In this paper, the Author proposed new Algorithms

– Item-Based CF

– Performance & Quality can be improved


1-6. Collaborative Filtering Process

i1 i2 ・・ i n

u1 a1,2

u2

u3

:

um

Input Data

•U ={ u1,u2,..,um}• I ={i1,i2,..,in}• Iui : item where user ui

evalues, Iui⊆ I• ai,j : evaluation of item ijby user ui

CF-Algorithm

Prediction

Recommendation(Top-N Recommendation)

Output Interface

Pa,j

• Predicted the degree of likeness of item ij by the user ua

• Ir ∩Iua = Φ

A list of N-items that the user will

like the most(Ir⊂I)

•Ir ∩Iua = Φ

User – Item Matrices


1-7.Variation of the CF-Algorithm

CF- Algorithm

Memory Based Approach Model Based Approach

•Procedure(Nearest Neighbor) 1. The system defines a set of

users known as neighborsat on-line

2. The system produces a prediction or top-n recommendation

• Procedure1. The system develops a

model of user ratings at off-line

2. By using the model, the system produce a prediction or top n recommendation

• How developing the mode ? • Bayesian Network• clustering


1-8.What ‘s online and offline ?

Off-line Computation On-line Computation

At a suitable interval,

offline computation is

performed automatically

When a user used the

system, online

Computation is

performed quickly

EX:Google

• Indexing

• Crowling

• Ranking

If you input a query, the

search engine output the

result.


1-9.the problem of the basic CF

Weakness of

the Nearest

Neighbor

Accuracy

Sparsity of user-item matrices:

many users may have purchased

well under 1% of the all items →

accuracy of Nearest Neighbor

algorithm may be poor

Scalability

With millions of users and

items, Nearest Neighbor

algorithm may suffer serious

scalability problem

We need new CF-Algorithms………..


ITEM-BASED CF

Chap.2


2-1. Overview of Item base CF

Off-line Computation On-line Computation

Item Similarity Computation Prediction Computation

Si,j : Similarity between item ii and ij •Pu,i is the degree of the likeness item-i by user-u ,based on the similarity between items,S

i1 i2 ・・ i n

u1 R1,2

u2

u3

:

um

S2n


2-2. Item Similarity Computation

• Cosine-Based Similarity

• Correlation-based Similarity

• Adjusted Cosine Similarity

The Difference in rating scale between defferent users


2-3.Prediction Computation

• Weighted Sum

• Regression

– Ru,n is calculated by Regression model

– Ri: Target item’s rating(explaining variable)

– Rn: Similar item’s rating (explained variable)

normalization coefficient

•N is the set of item that is very

similar with item I

• |N| : neighbor size


2-4. Time Complexity(1/2)

On-line Computation

User Similarity Computation

Prediction Computation

•Computing 1 user-user similarity, Recommend System scan n scores.→ O(n)• Recommend System must computing m × m user-user similarity. →O(m×m)

Action

TimeComplexity

O(m2n) + O(m)

• Computing 1 Pi,j-Value, Recommend System scan m user-user similarity → O(m)

Time complexity of Nearest Neibhor is…..


2-4. Time Complexity(2/2)

On-line Computation

Item Similarity Computation

Prediction Computation

Item-Item Similarity is static as opposed the User Similarity → It It’s possible to precompute item Similarity ( = model )

Action

TimeComplexity

O(n)

Computing 1 Pi,j-Value, Recommend System scan n item similarity → O(n)

Time complexity of Item-Based CF is better Performance than Neaest Neighbor

Off-line Computation


EXPERIMENTAL PROCEDURE

Chap.3


3-1. Experimental Procedure

the data set is divided into a train and a test portion

The Follow parameters is decided.

• Similarity Algorithms

• Train/ Test Ratio(x) : Sparsity level in data

• neighborhood size

user item rating

u1 i2 3u2 i1 2

u6 i3 3

TestTrain

Evaluation

Parameter Learning

1.Data Dividing

2.To fix the optimal values of a parameter

3.Full Experiment To evalue Item based CF, the follow value is measured

• Performance

• Quality


3-2. Data Sets

• Data Sets

– Data from website “ MovieLens”

– MovieLens is web based recommender system

– Hundreds of users visit MovieLens to rate and

receive recommendations for movies.

– A data set was converted into a user-item

matrix( 943user × 1682 columns )


3-3. Evaluation Metrics

• To evaluating the quality of a recomender system, we use MAE as evaluation metrics.

• MAE: Mean Absolute Error

– pi: Predicted Rating for item I (predicted based on a train data)

– qi: true Rating for item I (from a test data)

– The lower the MAE, the more accurately the recommendation engine predicts user ratings.


EXPERIMENTAL RESULTS

Chap.4


4-1.Optimal Values of a parameter(1/2)

Item-Similarity Algorithms =

Adjusted cosine is the best

quality

Train-test ratio (x) = 0.8 as an

optimum value


4-1.Optimal Values of a parameter(2/2)

Considering both trends,

Optimal choise of

Neighborhood Size

Is 30

In Full Experiment, basic

parameter is as follows.

• Similarity Algorithms:

Adjusted Cosine

• test/train ratio: 0.8

• neighborhood size : 30


4-2. Quality

• Quality

• Item-Based CF ( weighted sum ) out perform the nearest-neighbor• Item-Based CF (regression ) out perform the other two cases at low values of x and at low neighborhood size


4-3. Performance(1/2)

• model size:– Full model: At item similarity computation,

all item – item similarity(1682×1682) is computed .

– Model size = 200: At item similarity computation, 200 item – 200 item similarity (200×200 ) is computated .

• If model size is small , Good quality is consistent ? – Other model based Approach is consistent

– If it is consistent, online performance is higher than full- model case

• Result: – if model size is 100 ~ 200, it’s possible to

obtain resonably good prediction quality

In the case of not using all item-item similarity , the accurarcy of

prediction don’t down and the performance improve.


CONCLUSIONS

Chap.5


5. Conclusion

• Quality– Item-based CF provides better quality of predictions

than nearest neighbor Algorithms.• Independent of Neighborhood size and train/test ratio

– The improvement in quality is not large

• Performance– Item-Similarity Computation can be pre-computed

• Item-similarity is static

– High online Performance

– It is possible to retain only a small subset of items and produce good prediction quality& high Performance


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