SURBAY
ITEM-BASED COLLABORATIVE
FILTERING RECOMMENDATION
ALGORITHMS

Outline
 Reference
 What is item-based collaborative filtering
recommendation algorithms?
 User-based vs item-based algorithms
 Methods
 how to implement item-based algorithms
 Similarity
 prediction
 Conclusion

Reference
 [1]SARWAR, B., KARYPIS, G., KONSTAN, J., AND
RIEDL, J. 2001. Item-based collaborative filtering
recommendation algorithms.
 [2]LINDEN, G., SMITH, B., and YORK, J. Amazon.com
Amazon.com Recommendations: Item-to-item
Collaborative filtering
 [3]DESHPANDE, M, and KARYPIS, G. Item-Based
Top-N Recommendation Algorithms

What is this?
 Recommendation Systems
 apply knowledge discovery techniques to the problem
of making personalized recommendations for customers
 Collaborative Filtering
 Isto suggest new items or to predict the utility of a
certain item for a particular user
E.g., Recommendation in Amazon.co.jp
Collabora
DB tive
Filtering

User-based vs. Item-based#1
Not similar.
Not similar Similar!
Similar! Not similar
User: have
Not similar opinion or
history of buying.
 User-based collaborative filtering systems
 is
to find preferences of a user with similarity of each
users history or opinion of buying.
 have been very successful in past. But according to
increase user and items, this system had two problems.

User-based vs. Item-based#2
 Two problems are
 Sparsely
 e-commerce has large item set but even active users may have
purchased well under 1% of the items.
 Scalability
 This algorithms will suffer scalability to grows the number of users
and items
 As a result, the performance and quality of user-based
system are poor.

User-based vs. Item-based#3
 Item-based techniques is to
 Analyze the user-item matrix to identify relationships
between different items
 Use these relationships to indirectly compute
recommendations for users.
 Item-based makes up for the fault of User-based.
 This algorithms calculate by the similarity of each items,
the problem of sparsely and scalability was solved.

Methods
 Order
• Create user-item matrix as a ratings matrix
1
• Compute similarity of each items
2
• Decide prediction for target user.
3
User-Item matrix
Item_1 I_2 I_3 I_4
User_1 3 5
U_2 1 2 3
U_3 5 4 2 Prediction
U_4 5 1 Recommendation

Similarity
 Similarity is calculated by items opinion or buying
of users.
 Measurements
 Cosine based Similarity
 Conditional Probability-Based Similarity
 Correlation-based Similarity
 Adjusted Cosine Similarity
E.g., Cosine based Similarity
ij
Sim(i, j )  cos( i, j) 
i  j

Prediction
 Predictions is calculated based on similarity of items.
 When target user bought one item, he/she get prediction in
high similarity items which target user have not purchase or
vote rate.
 Measurements
 Weighted Sum
 Regression
Weighted Sum
Pu ,i 
Allsimilar
Items, N
( si , N * Ru , N )
 AllSimilar
Items, N
( si , N )

Amazon.com
 Amazon.com uses recommendations as a targeted marketing tool in many
email campaigns and on most of its web sites’ page.
 Item-to-item collaborative filtering scales to massive data sets and produces
high-quality recommendations in real time.
 Offline
 The algorithm builds a similar-items table by finding items that customers tend to
purchase together.
 To create this matrix by offline, this algorithms scalability and performance are
efficient.
 Online
 The online component-looking up similar items for the user’s purchases and
ratings-scales independently of the catalog size or the total number of customers.
 It is dependent only on how many titles the user has purchased or rated.

Conclusion
 Item-based Collaborative filtering algorithm is
higher quality rather than user-based collaborative
filtering using similarity of items.
 Item-based algorithms is used by a lot of e-
commerce site.