Recommender Systems
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RECOMMENDATION SYSTEMS Usman Sharif
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Transcript of Recommender Systems
RECOMMENDATION SYSTEMSUsman Sharif
Why recommendation systems?
Provide a better experience to your users.
Understand the behavior and patterns of users.
Enables an opportunity to re-engage inactive users.
Boost sales Better than a search feature
How some companies are using Recommendation Systems - Amazon
How some companies are using Recommendation Systems - Gmail
A simple recommendation system
Consider the following scenario A library has books and has members Members can have books issued The library wants to build a
recommender system to recommend books to their members
Scoring Matrices
Book 1 Book 2 Book 3 Book 4
User 1 X X
User 2 X
User 3 X X
User 4 X X X
User 5 X X
Book 1 Book 2 Book 3 Book 4
Book 1 4 1 2 1
Book 2 1 2 0 1
Book 3 2 0 2 1
Book 4 1 1 1 2
Using the scoring matrices If a user has read Book 1 recommend Book 3,
2, 4. If a user has read Book 2 recommend Book 1,
4, 3. If a user has read Book 3 recommend Book 1,
4, 2. If a user has read Book 4 recommend Book 1,
2, 3.
Advantages
Very simple to understand and implement.
Works really well if you’re interested in looking at user’s one activity to recommend further.
Disadvantages
Cannot work for a new user with no history.
In a real world scenario where there are thousands of books and thousands of members, there are bound to be too many zeroes (a sparse matrix).
Does not consider more than 1 item.
Another Try Our Books records might look like this:BookId
Title Genre Writer Language
1 The Great Gatsby Classic F Scott Fitzgerald
English
2 Nine Stories Short Stories
J D Salinger English
3 The Sun Also Rises Classic Ernest Hemingway
English
4 The Hunger Games Action Suzanne Collins
English
5 The Ambler Warning Thriller Robert Ludlum English
6 The Catcher in the Rye Classic J D Salinger English
7 To Kill a Mockingbird Classic Harper Lee English
Create an Item Similarity Matrix
Book 1
Book 2
Book 3
Book 4
Book 5
Book 6
Book 7
Book 1
3 1 2 1 1 2 2
Book 2
1 3 1 1 1 2 1
Book 3
2 1 3 1 1 2 2
Book 4
1 1 1 3 1 1 1
Book 5
1 1 1 1 3 1 1
Book 6
2 2 2 1 1 3 2
Book 7
2 1 2 1 1 2 3
• This would always be a square (n x n) matrix.• Each cell has the count of similar attributes (excluding unique attributes).• In general any measure for similarity can be used here.
To Recommend
Look at what a user has previously read.
Use the values from the similarity matrix and recommend books based on how similar it is to the book the user has already read.
Advantages
Recommendations can be pre-computed for a very large Item base.
Fast lookups can be built to perform recommendations.
For example, if a user is seeing the page of Book 3, you may want to recommend them Books 1, 6 and 7.
Would work for new/non-registered users.
Disadvantage
Does not consider the user’s history. Instead looks at a collective trend.
Another Approach - The Users Our Users records might look like
this:UserId Gender Age Location
1 Male 34 Pakistan
2 Female 28 Pakistan
3 Male 38 India
4 Male 32 India
5 Female 21 Pakistan
6 Female 24 Pakistan
The User BorrowingUserId BookId
1 3
1 7
2 2
3 1
3 5
3 7
4 6
4 7
5 2
6 4
6 6
6 7
Transforming User Borrowing
User 1 User 2 User 3 User 4 User 5 User 6
Book 1
X
Book 2
X X
Book 3
X
Book 4
X
Book 5
X
Book 6
X X
Book 7
X X X X
• Issue with too many zero values.• Any solutions?
Transform the Users Records Consider Age as a discrete column
with ranges like {0-10, 11-20, 21-30, 31-40, …} so that we can create some partitions like this:PartitionId
Gender AgeGroup Location
1 Male 31-40 Pakistan
2 Female 21-30 Pakistan
3 Male 31-40 India
Recreate User Borrowing using Partition Information
Lesser zero valued records (11/21 compared to 30/42 previously)
Much less columns than we previously had!
The notation has been changed from ‘X’ to count.
Partition 1
Partition 2
Partition 3
Book 1
1
Book 2
2
Book 3
1
Book 4
1
Book 5
1
Book 6
1 1
Book 7
1 1 2
To Recommend
See what partition a user belongs to. Look at the column of that partition
and sort the books in descending order based on their frequency count.
Advantages
Continues to improve over time. More partitions can be added over
time. Instead of using a collective scoring,
the technique partitions the user base into ‘similar’ users.
The technique can easily be extended on the item side and rather than having books as rows, we can have book clusters.
Disadvantages
Needs some seed data to start. Requires some transformations. Can become very complex as the
number of users/items grow.
Evaluating Performance (Metrics)
Almost any Information Retrieval metric can be used.
Three interesting ones: Accuracy Coverage Normalized Distance Based Performance
Measure (NDPM)
Accuracy
UserId BookId Rank Response
1 3 1 Yes
1 2 2 No
2 7 1 No
2 5 2 Yes
3 3 1 No
3 7 2 No
• Takes into account the order in which recommendations are shown to users and how they responded to them.
• For rank position = 1:• Acc(1) = # of Positive responses with rank less than
or equal to 1 / total recommendations with rank less than or equal to 1
• Therefore, Acc(1) = 1 / 3 = 33.33%• Similarly, Acc(2) = 2 / 6 = 33.33%
Coverage Shows the coverage of items that appear in the
recommendations for all users. For rank position = 1:
Cov(1) = Unique items in recommendations with rank less than or equal to 1 / total items.
Therefore, Cov(1) = 2 / 7 = 28.57% Similarly, Cov(2) = 4 / 7 = 57.14%
UserId BookId Rank Response
1 3 1 Yes
1 2 2 No
2 7 1 No
2 5 2 Yes
3 3 1 No
3 7 2 No
Normalized Distance Based Performance Measure (NDPM)
Assesses the quality of the measure of recommendation system taking into account the ordering in which items are shown.
NDPM = (C- + 0.5 x C+) / Cu
C- - is the number of recommended item pairs where user responded as (No, Yes).
C+ - is the number of recommended item pairs where user responded as (Yes, No).
Cu - is the number of all item pairs where the user’s response was not same.
In our example, C-(1) = 2, C+(1) = 2 and Cu(1) = 4 => NDPM(1) = (2 + 0.5 x 2) / 4 = 75% C-(2) = 0, C+(2) = 1 and Cu(2) = 1 => NDPM(2) = (0 + 0.5 x 1) / 1 = 50% NDPM = (0.75 + 0.5) / 2 = 62.5%
UserId BookId Rank Response
1 3 1 Yes
1 2 2 No
1 7 3 No
1 5 4 Yes
2 3 1 Yes
2 7 2 No
How to improve results
Ensure that you maintain a list of already seen recommendations for users and don’t recommend them back for some time.
Provide some sort of mechanism to user to provide information about what they’re looking for.
Infer the above from user searches.
Some standard algorithms
Item Hierarchy You bought a printer, you will also need ink.
Attribute-based recommendations You like reading classics, written by Salinger, you might like
“Catcher in the Rye”. Collaborative Filtering – User-User Similarity
People like you who read “The Hunger Games” also read “The Ambler Warning”.
Collaborative Filtering – Item-Item Similarity You like “Catcher in the Rye” so you will like “Nine Stories”.
Social + Interest Graph Based Your friends like “The Great Gatsby” so you will like “The Great
Gatsby” too. Model Based
Training SVM, LDA, SVD for implicit features.
Some Tools
Apache Mahout (Java)
Crab (Python)
Easyrec (RESTful API)
Questions??
Thankyou!
www.usman-sharif.com
@sharif_usman
