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IMDAMohammad Hamidi Esfahani
Recommendation Systems
‫اصفهانی‬ ‫حمیدی‬ ‫محمد‬
‫مردادماه‬1394
IMDAMohammad Hamidi Esfahani
Introduction
Recommender systems are widely used on the Web
for recommending products and services to users.
Most e-commerce sites have such systems.
These systems serve two important functions.
They help users deal with the information overload by giving
them recommendations of products, etc.
They help businesses make more profits, i.e., selling more
products.
IMDAMohammad Hamidi Esfahani
Ecommerce Concerns
How to collect more users ???
How to settled the users in our website ???
Which product to sell or resell ???
Which users are important ????
IMDAMohammad Hamidi Esfahani
Recommendation Systems
When ???
Which ???
How ???
FeedBack
Improve Recommendation
IMDAMohammad Hamidi Esfahani
Facts
Rating
Additional attributes
Likelihood
Item Similarity
User Similarity
Information Gathering
Biggest Problem: Cold Start
IMDAMohammad Hamidi Esfahani
Recommendation Systems(Conti..)
Recommendation Method
Collaborative
Content-Based
Knowledge-Based
Hybrid
IMDAMohammad Hamidi Esfahani
Collaborative Method
if user A and user B have a
purchase history that overlaps
strongly and user A has
recently bought an item that B
has not yet been, the basic
rationale is to propose this
item also to B
IMDAMohammad Hamidi Esfahani
Collaborative Method
Maintain a database of many users’ ratings of a variety
of items.
For a given user, find other similar users whose ratings
strongly correlate with the current user.
Recommend items rated highly by these similar users,
but not rated by the current user.
Almost all existing commercial recommenders use this
approach (e.g. Amazon).
IMDAMohammad Hamidi Esfahani
A 9
B 3
C
: :
Z 5
A
B
C 9
: :
Z 10
A 5
B 3
C
: :
Z 7
A
B
C 8
: :
Z
A 6
B 4
C
: :
Z
A 10
B 4
C 8
. .
Z 1
User
Database
Active
User
Correlation Match
A 9
B 3
C
. .
Z 5
A 9
B 3
C
: :
Z 5
A 10
B 4
C 8
. .
Z 1
Extract
Recommendations
C
IMDAMohammad Hamidi Esfahani
Collaborative Method
Weight all users with respect to similarity with the
active user.
Select a subset of the users (neighbors) to use as
predictors.
Normalize ratings and compute a prediction from a
weighted combination of the selected neighbors’
ratings.
Present items with highest predicted ratings as
recommendations.
IMDAMohammad Hamidi Esfahani
Typical Questions
How to find users with similar tastes to the user for whom we
need a recommendation?
How to measure similarity?
What should be done with new users, for whom a buying
history is not yet available?
How to deal with new items that nobody has bought yet?
What other techniques besides looking for similar users can
use for making a prediction about whether a certain user will
like an item?
IMDAMohammad Hamidi Esfahani
Content Based Method
if item A and item B have similar
attributes, the basic rationale is
to Recommend item B to whom
that buy item A
IMDAMohammad Hamidi Esfahani
Content Based Method (Conti..)
The key idea of content-based filtering approach is
based on similarity of item’s features.
The best idea in content-based recommendation
system, is to recommend similar items to user that
buy the first item previously.
IMDAMohammad Hamidi Esfahani
Content Based Method
Recommendations are based on information on the
content of items rather than on other users’ opinions.
Uses a machine learning algorithm to induce a profile of
the users preferences from examples based on a featural
description of content.
Some previous applications:
Newsweeder (Lang, 1995)
Syskill and Webert (Pazzani et al., 1996)
IMDAMohammad Hamidi Esfahani
Advantages of Content-Based Approach
No need for data on other users.
No cold-start or sparsity problems.
Able to recommend to users with unique tastes.
Able to recommend new and unpopular items
 No first-rater problem.
Can provide explanations of recommended items by
listing content-features that caused an item to be
recommended.
15
IMDAMohammad Hamidi Esfahani
Disadvantages of Content-Based Method
Requires content that can be encoded as
meaningful features.
Users’ tastes must be represented as a learnable
function of these content features.
Unable to exploit quality judgments of other users.
Unless these are somehow included in the content
features.
16
IMDAMohammad Hamidi Esfahani
Typical Questions
How can systems automatically acquire and
continuously improve user profiles?
How to determine which items match, or are at least
similar to or compatible with, a user’s interests?
What techniques can be used to automatically
extract or learn the item descriptions to reduce
manual annotation?
IMDAMohammad Hamidi Esfahani
Knowledge
Based
(Rule Based)
Typical customers buy a new camera only once
every few years, so the recommender system
cannot construct a user profile or propose
cameras that others liked, which would result in
proposing only top-selling items.
IMDAMohammad Hamidi Esfahani
Knowledge Based Method
The recommender system typically makes use of
additional, often manually provided, information
about both the current user and the available items
User Interaction
Direct
Indirect
IMDAMohammad Hamidi Esfahani
Typical Questions
 What kinds of domain knowledge can be represented in a knowledge
base?
 What mechanisms can be used to select and rank the items based on the
user’s characteristics?
 How to acquire the user profile in domains in which no purchase history is
available, and how to take the customer’s explicit preferences into
account?
 Which interaction patterns can be used in interactive recommender
systems?
 Finally, in which dimensions can personalize the dialog to maximize the
precision of the preference elicitation process?
IMDAMohammad Hamidi Esfahani
Hybrid Approaches
One obvious solution is to
combine different techniques
to generate better or more
precise recommendations
IMDAMohammad Hamidi Esfahani
Typical Questions
Which techniques can be combined, and what are
the prerequisites for a given combination?
Should proposals be calculated for two or more
systems sequentially, or do other hybridization
designs exist?
How should the results of different techniques be
weighted and can they be determined dynamically?
IMDAMohammad Hamidi Esfahani
Sample Hybrid Method
Proposed hybrid recommendation system method
Knowledge-
basedpart
Collaborative
part
Content-basedpart
User
interaction-
basedpart
Item’s
features
User’s
properties
C-Means Clustering Method
Extract Weighted Map
Users
History
Extract Rules
Prioritize Rules
Users Interaction
Like/Dislike
IMDAMohammad Hamidi Esfahani
Proposed Method (Conti..)
First off all, Extract the clusters on items and users
based an their profile features and the belonging
factor for each item or user
Second, Extract the relations and relation weight
between clusters
IMDAMohammad Hamidi Esfahani
Proteins
Classifying VS Clustering
 Objects characterized by one or more features
 Classification
 Have labels for some points
 Want a “rule” that will accurately assign labels to new
points
 Supervised learning
 Clustering
 No labels
 Group points into clusters based on how “near” they
are to one another
 Identify structure in data
 Unsupervised learning
Genes
IMDAMohammad Hamidi Esfahani
Similarity
KNN (K-Nearest Neighbor)
K-Means
C-Means
IMDAMohammad Hamidi Esfahani
K-Nearest Neighbor
Features
All instances correspond to points in an n-dimensional
Euclidean space
Classification is delayed till a new instance arrives
Classification done by comparing feature vectors of the
different points
Target function may be discrete or real-valued
IMDAMohammad Hamidi Esfahani
1-Nearest Neighbor
IMDAMohammad Hamidi Esfahani
3-Nearest Neighbor
IMDAMohammad Hamidi Esfahani
K-Nearest Neighbor
An arbitrary instance is represented by (a1(x), a2(x), a3(x),..,
an(x))
ai(x) denotes features
Euclidean distance between two instances
d(xi, xj)=sqrt (sum for r=1 to n (ar(xi) - ar(xj))2)
Continuous valued target function
 mean value of the k nearest training examples
IMDAMohammad Hamidi Esfahani
The K-Means Clustering Method
 Given k, the k-means algorithm is implemented in four
steps:
1. Partition objects into k nonempty subsets
2. Compute seed points as the centroids of the clusters of the current partition
(the centroid is the center, i.e., mean point, of the cluster)
3. Assign each object to the cluster with the nearest seed point
4. Go back to Step 2, stop when no more new assignment
IMDAMohammad Hamidi Esfahani
K-means Clustering
IMDAMohammad Hamidi Esfahani
K-means Clustering
IMDAMohammad Hamidi Esfahani
K-means Clustering
IMDAMohammad Hamidi Esfahani
K-means Clustering
IMDAMohammad Hamidi Esfahani
K-means Clustering
IMDAMohammad Hamidi Esfahani
Fuzzy C-means Clustering
Fuzzy c-means (FCM) is a method of clustering
which allows one piece of data to belong to two or
more clusters.
This method (developed by Dunn in 1973 and
improved by Bezdek in 1981) is frequently used in
pattern recognition.
IMDAMohammad Hamidi Esfahani
Fuzzy C-means Clustering
IMDAMohammad Hamidi Esfahani
Fuzzy C-means Clustering
IMDAMohammad Hamidi Esfahani
Fuzzy C-means Clustering
IMDAMohammad Hamidi Esfahani
Fuzzy C-means Clustering
IMDAMohammad Hamidi Esfahani
Fuzzy C-means Clustering
IMDAMohammad Hamidi Esfahani
Fuzzy C-means Clustering
IMDAMohammad Hamidi Esfahani
Fuzzy C-means Clustering
IMDAMohammad Hamidi Esfahani
Fuzzy C-means Clustering
IMDAMohammad Hamidi Esfahani
Proposed Method (Conti..)
item 𝐼 𝑎 belongs to cluster 𝐶𝑖 by 𝐵(𝐶𝑖𝑎) degree
Item 𝐼 𝑏 belong to cluster 𝐶𝑖 by 𝐵(𝐶𝑖𝑏) degree
User 𝑈 𝑎 belongs to cluster 𝐶 𝑢 by 𝐵(𝐶 𝑢𝑎) degree
User 𝑈 𝑏 belongs to cluster 𝐶 𝑢 by 𝐵(𝐶 𝑢𝑏) degree
𝑈 𝑎 buy 𝐼 𝑎 and user 𝑈 𝑏 don’t buy any item and item
𝐼 𝑏 never buy
IMDAMohammad Hamidi Esfahani
Proposed Method (Conti..)
 cluster 𝐶𝑖 on items has a relation to cluster 𝐶 𝑢 on users by 𝑀(𝐶𝑖𝑢)
 𝑀 𝐶𝑖𝑢 =
𝑥
𝑖𝑡𝑒𝑚𝑠
𝑦
𝑢𝑠𝑒𝑟𝑠 𝐵 𝐶 𝑢𝑥 ∗ 𝐵(𝐶 𝑖𝑦)
𝑚∗𝑛
 m => the count of users
 n => the count of items.
 Then, a rule generated for recommending items in cluster 𝐶𝑖 on items to
users in cluster 𝐶 𝑢 on users. So, user 𝑈 𝑏 have a relation with item 𝐼 𝑏
 𝑅𝑒𝑐 𝑈 𝑏, 𝐼 𝑏 = 𝑀 𝐶𝑖𝑢 ∗ 𝐵 𝐶𝑖𝑏 ∗ 𝐵 𝐶 𝑢𝑏
IMDAMohammad Hamidi Esfahani
SahamKaav.iR
proposed
method
with c-means
proposed
method
with k-means
random
recommendation
Number of
clusters of users
14 10 -
Number of
clusters of Stocks
56 48 -
LF (Like Factor ) 78% 53% 32%
IMDAMohammad Hamidi Esfahani
Q & AMohammad Hamidi Esfahani
m.hamidi.es@gmail.com
Twitter: @haj_mamed

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Recommendation systems

  • 1. IMDAMohammad Hamidi Esfahani Recommendation Systems ‫اصفهانی‬ ‫حمیدی‬ ‫محمد‬ ‫مردادماه‬1394
  • 2. IMDAMohammad Hamidi Esfahani Introduction Recommender systems are widely used on the Web for recommending products and services to users. Most e-commerce sites have such systems. These systems serve two important functions. They help users deal with the information overload by giving them recommendations of products, etc. They help businesses make more profits, i.e., selling more products.
  • 3. IMDAMohammad Hamidi Esfahani Ecommerce Concerns How to collect more users ??? How to settled the users in our website ??? Which product to sell or resell ??? Which users are important ????
  • 4. IMDAMohammad Hamidi Esfahani Recommendation Systems When ??? Which ??? How ??? FeedBack Improve Recommendation
  • 5. IMDAMohammad Hamidi Esfahani Facts Rating Additional attributes Likelihood Item Similarity User Similarity Information Gathering Biggest Problem: Cold Start
  • 6. IMDAMohammad Hamidi Esfahani Recommendation Systems(Conti..) Recommendation Method Collaborative Content-Based Knowledge-Based Hybrid
  • 7. IMDAMohammad Hamidi Esfahani Collaborative Method if user A and user B have a purchase history that overlaps strongly and user A has recently bought an item that B has not yet been, the basic rationale is to propose this item also to B
  • 8. IMDAMohammad Hamidi Esfahani Collaborative Method Maintain a database of many users’ ratings of a variety of items. For a given user, find other similar users whose ratings strongly correlate with the current user. Recommend items rated highly by these similar users, but not rated by the current user. Almost all existing commercial recommenders use this approach (e.g. Amazon).
  • 9. IMDAMohammad Hamidi Esfahani A 9 B 3 C : : Z 5 A B C 9 : : Z 10 A 5 B 3 C : : Z 7 A B C 8 : : Z A 6 B 4 C : : Z A 10 B 4 C 8 . . Z 1 User Database Active User Correlation Match A 9 B 3 C . . Z 5 A 9 B 3 C : : Z 5 A 10 B 4 C 8 . . Z 1 Extract Recommendations C
  • 10. IMDAMohammad Hamidi Esfahani Collaborative Method Weight all users with respect to similarity with the active user. Select a subset of the users (neighbors) to use as predictors. Normalize ratings and compute a prediction from a weighted combination of the selected neighbors’ ratings. Present items with highest predicted ratings as recommendations.
  • 11. IMDAMohammad Hamidi Esfahani Typical Questions How to find users with similar tastes to the user for whom we need a recommendation? How to measure similarity? What should be done with new users, for whom a buying history is not yet available? How to deal with new items that nobody has bought yet? What other techniques besides looking for similar users can use for making a prediction about whether a certain user will like an item?
  • 12. IMDAMohammad Hamidi Esfahani Content Based Method if item A and item B have similar attributes, the basic rationale is to Recommend item B to whom that buy item A
  • 13. IMDAMohammad Hamidi Esfahani Content Based Method (Conti..) The key idea of content-based filtering approach is based on similarity of item’s features. The best idea in content-based recommendation system, is to recommend similar items to user that buy the first item previously.
  • 14. IMDAMohammad Hamidi Esfahani Content Based Method Recommendations are based on information on the content of items rather than on other users’ opinions. Uses a machine learning algorithm to induce a profile of the users preferences from examples based on a featural description of content. Some previous applications: Newsweeder (Lang, 1995) Syskill and Webert (Pazzani et al., 1996)
  • 15. IMDAMohammad Hamidi Esfahani Advantages of Content-Based Approach No need for data on other users. No cold-start or sparsity problems. Able to recommend to users with unique tastes. Able to recommend new and unpopular items  No first-rater problem. Can provide explanations of recommended items by listing content-features that caused an item to be recommended. 15
  • 16. IMDAMohammad Hamidi Esfahani Disadvantages of Content-Based Method Requires content that can be encoded as meaningful features. Users’ tastes must be represented as a learnable function of these content features. Unable to exploit quality judgments of other users. Unless these are somehow included in the content features. 16
  • 17. IMDAMohammad Hamidi Esfahani Typical Questions How can systems automatically acquire and continuously improve user profiles? How to determine which items match, or are at least similar to or compatible with, a user’s interests? What techniques can be used to automatically extract or learn the item descriptions to reduce manual annotation?
  • 18. IMDAMohammad Hamidi Esfahani Knowledge Based (Rule Based) Typical customers buy a new camera only once every few years, so the recommender system cannot construct a user profile or propose cameras that others liked, which would result in proposing only top-selling items.
  • 19. IMDAMohammad Hamidi Esfahani Knowledge Based Method The recommender system typically makes use of additional, often manually provided, information about both the current user and the available items User Interaction Direct Indirect
  • 20. IMDAMohammad Hamidi Esfahani Typical Questions  What kinds of domain knowledge can be represented in a knowledge base?  What mechanisms can be used to select and rank the items based on the user’s characteristics?  How to acquire the user profile in domains in which no purchase history is available, and how to take the customer’s explicit preferences into account?  Which interaction patterns can be used in interactive recommender systems?  Finally, in which dimensions can personalize the dialog to maximize the precision of the preference elicitation process?
  • 21. IMDAMohammad Hamidi Esfahani Hybrid Approaches One obvious solution is to combine different techniques to generate better or more precise recommendations
  • 22. IMDAMohammad Hamidi Esfahani Typical Questions Which techniques can be combined, and what are the prerequisites for a given combination? Should proposals be calculated for two or more systems sequentially, or do other hybridization designs exist? How should the results of different techniques be weighted and can they be determined dynamically?
  • 23. IMDAMohammad Hamidi Esfahani Sample Hybrid Method Proposed hybrid recommendation system method Knowledge- basedpart Collaborative part Content-basedpart User interaction- basedpart Item’s features User’s properties C-Means Clustering Method Extract Weighted Map Users History Extract Rules Prioritize Rules Users Interaction Like/Dislike
  • 24. IMDAMohammad Hamidi Esfahani Proposed Method (Conti..) First off all, Extract the clusters on items and users based an their profile features and the belonging factor for each item or user Second, Extract the relations and relation weight between clusters
  • 25. IMDAMohammad Hamidi Esfahani Proteins Classifying VS Clustering  Objects characterized by one or more features  Classification  Have labels for some points  Want a “rule” that will accurately assign labels to new points  Supervised learning  Clustering  No labels  Group points into clusters based on how “near” they are to one another  Identify structure in data  Unsupervised learning Genes
  • 26. IMDAMohammad Hamidi Esfahani Similarity KNN (K-Nearest Neighbor) K-Means C-Means
  • 27. IMDAMohammad Hamidi Esfahani K-Nearest Neighbor Features All instances correspond to points in an n-dimensional Euclidean space Classification is delayed till a new instance arrives Classification done by comparing feature vectors of the different points Target function may be discrete or real-valued
  • 30. IMDAMohammad Hamidi Esfahani K-Nearest Neighbor An arbitrary instance is represented by (a1(x), a2(x), a3(x),.., an(x)) ai(x) denotes features Euclidean distance between two instances d(xi, xj)=sqrt (sum for r=1 to n (ar(xi) - ar(xj))2) Continuous valued target function  mean value of the k nearest training examples
  • 31. IMDAMohammad Hamidi Esfahani The K-Means Clustering Method  Given k, the k-means algorithm is implemented in four steps: 1. Partition objects into k nonempty subsets 2. Compute seed points as the centroids of the clusters of the current partition (the centroid is the center, i.e., mean point, of the cluster) 3. Assign each object to the cluster with the nearest seed point 4. Go back to Step 2, stop when no more new assignment
  • 37. IMDAMohammad Hamidi Esfahani Fuzzy C-means Clustering Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method (developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition.
  • 46. IMDAMohammad Hamidi Esfahani Proposed Method (Conti..) item 𝐼 𝑎 belongs to cluster 𝐶𝑖 by 𝐵(𝐶𝑖𝑎) degree Item 𝐼 𝑏 belong to cluster 𝐶𝑖 by 𝐵(𝐶𝑖𝑏) degree User 𝑈 𝑎 belongs to cluster 𝐶 𝑢 by 𝐵(𝐶 𝑢𝑎) degree User 𝑈 𝑏 belongs to cluster 𝐶 𝑢 by 𝐵(𝐶 𝑢𝑏) degree 𝑈 𝑎 buy 𝐼 𝑎 and user 𝑈 𝑏 don’t buy any item and item 𝐼 𝑏 never buy
  • 47. IMDAMohammad Hamidi Esfahani Proposed Method (Conti..)  cluster 𝐶𝑖 on items has a relation to cluster 𝐶 𝑢 on users by 𝑀(𝐶𝑖𝑢)  𝑀 𝐶𝑖𝑢 = 𝑥 𝑖𝑡𝑒𝑚𝑠 𝑦 𝑢𝑠𝑒𝑟𝑠 𝐵 𝐶 𝑢𝑥 ∗ 𝐵(𝐶 𝑖𝑦) 𝑚∗𝑛  m => the count of users  n => the count of items.  Then, a rule generated for recommending items in cluster 𝐶𝑖 on items to users in cluster 𝐶 𝑢 on users. So, user 𝑈 𝑏 have a relation with item 𝐼 𝑏  𝑅𝑒𝑐 𝑈 𝑏, 𝐼 𝑏 = 𝑀 𝐶𝑖𝑢 ∗ 𝐵 𝐶𝑖𝑏 ∗ 𝐵 𝐶 𝑢𝑏
  • 48. IMDAMohammad Hamidi Esfahani SahamKaav.iR proposed method with c-means proposed method with k-means random recommendation Number of clusters of users 14 10 - Number of clusters of Stocks 56 48 - LF (Like Factor ) 78% 53% 32%
  • 49. IMDAMohammad Hamidi Esfahani Q & AMohammad Hamidi Esfahani m.hamidi.es@gmail.com Twitter: @haj_mamed