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Recommendation Systems in Mobile Commerce Presented by Rachana Chandrashekar(7487187)...
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Transcript of Recommendation Systems in Mobile Commerce Presented by Rachana Chandrashekar(7487187)...
Recommendation Systems in Mobile Commerce
Presented byRachana Chandrashekar(7487187)
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1. INTRODUCTION
2. OVERVIEW
3. RECOMMENDER SYSTEM MODEL
OUTLINE
4. RECOMMENDATION ALGORITHMS
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6. CHALLENGES IN MOBILE COMMERCE
7. CONCLUSION
5. CHALLENGES OF RECOMMENDER SYSTEMS
OUTLINE
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INTRODUCTION
The Paradox of Choice
Overwhelming number of options to consider Lack of effective system support in making decisions Too many options can make your visitors too confused and undecided
Only 10% of products on an online retail store garner 75% of page views
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OVERVIEW
What are recommendation systems?
A recommendation system provides information or items that are likely to be
of interest to a user in an automated fashion.
Recommendation systems help match users with items
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WHY DO WE NEED RECOMMENDATION SYSTEMS?
Value for the Customer- Find things that are interesting- Narrow down the set of choices- Help explore the space of options- Reduce cognitive load on users
Value for the provider- Additional and unique personalized service for the customer- Increase trust and customer loyalty- Increase sales, click through rates etc.- Opportunities for promotion, persuasion- Obtain more knowledge about customers
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MORE EXAMPLES..
Netflix predicts other “Movies You Love” based on past numeric
ratings (1-5 stars)
Recommendations drive 60% of Netflix’s DVD rentals
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RECOMMENDER SYSTEM MODEL
Candidate Generation
Rank
User Feedback
Filtering
Users Items
Automatically identify items of interest to users(Focus of talk)
Filters: near duplicates, already seen, dismissed
Recommendations based on temporal, geo-location and personalization
Track user feedback, likes, dislike, ratings
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RECOMMENDATION ALGORITHMS
Collaborative filtering (CF)
Hypothesis : Similar users tend to like similar items
Two forms of CF-Item-based collaborative filtering-User-based collaborative filtering
Data Collection Methods- Explicit feedback
Example: ratings, dismiss- Implicit feedback
Example: number of views, purchases
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DATA REPRESENTATION
Items : i1, i2, i3 …. in
User u1,u2,u3….un has provided ratings on items
Example of User/Movie Ratings Matrix:
Alice Bob Charlie Dave
Harry Potter … 3 5 2 3
American Pie 4 4 2 -
Twilight … 5 1 - -
Matrix - 1 1 5
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A NAÏVE RECOMMENDATION SYSTEM
1. Aggregate ratings for each item
2. Recommend item with maximum rating
score(i,u) = f(i) =
Does everybody like Harry Potter movies?
Historical information about users is important!
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Predict user’s rating for an item i based on his rating for other item Given a user u with I(u) preferred items
score(i,u) = sim(i,j)
Rating provided by user u for item j
Similarity between
items i and j
ITEM-BASED COLLABORATIVE FILTERING
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EXAMPLE : ITEM-BASED CF
Given user with ratings for items X and Y
Items N and S with similarities
score(u,N) = 1.0*0.8 + 0.3*0.3 = 0.89
score(u,S) = 0.2*0.8 + 0.3*0.8 = 0.4
Harry Potter (X) The Matrix (Y)
rating 0.8 0.3
Item Harry Potter (X) The Matrix (Y)
The Chronicles of Narnia (N)
1.0 0.3
Star Wars (S) 0.2 0.8
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COMPUTING SIMILARITY BETWEEN ITEMS
Cosine Similarity
- Items are represented as u-dimensional vectors over user space
- Similarity is cosine of the angle between two vectors
- Score ranges between 1 (perfect) and -1 (opposite)
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JACCARD SIMILARITY MEASURE
Measures the similarity between finite sample sets
J(A,B) =
Defined as the size of intersection divided by the size of the union of the
sample sets
Sample sets of Items :
A ={Item1,Item3,Item6}
B ={Item1,Item2,Item6}
J(A,B) = = 0.5
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USER BASED COLLABORATIVE FILTERING
K – nearest neighbors ( KNN )- Group users into different clusters
Hard clustering Soft clustering
Users Clusters Items
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CONTENT-BASED RECOMMENDATION
Collaborative filtering does not require any information about the items
- However, it might be reasonable to exploit such information
-E.g. Recommend fantasy novels to people who liked fantasy novels in
the past
What do we need?
- Some information about the available items such as the genre
(content)
- Some sort of user profile describing what the user likes
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HYBRID RECOMMENDER SYSTEMS
• Combination of collaborative filtering and content based
filtering
• Idea of crossing two or more implementations
• Hybrid features
- Social Features
Movies liked by user
- Content features
Dramas liked by user
- Hybrid features
User who like many movies that are dramas
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CHALLENGES AND INTERESTING PROBLEMS OF RECOMMENDER SYSTEMS
Data sparsity
-Users rarely purchase, rate or click
The more you see the less you know
- Increasing users or items increase the dimensions we need to learn
Cold-start problem
- No historical information for new users or items
Scalability
- Increase in the size of matrix
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CHALLENGES IN DESIGNING RECOMMENDER SYSTEMS FOR MOBILE USERS
• Size of the display, small screen devices
• Limited input and interaction capabilities
• Mobile users have shorter browsing sessions
• Lack of standardization of the browsing tools
• Cost of interaction
Exclusive characteristics :
• Location awareness
• Ubiquity
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CONCLUSION
Recommender systems are a huge success in E-commerce sites
Recommendation systems in mobile commerce have to overcome
obstacles
Mobile devices coupled with Recommender systems would be key
tools for business applications
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Question 1
In item based collaborative filtering, based on the user’s previous rating, recommend the most appropriate item to the user A.
Similarity with previously purchased items:
score (u,B) = 0.8*1.0 + 0.2*0.3 = 0.86
score(u,T) = 0.8*0 + 0.2*0.9 = 0.18
The item blueberry is recommended to the user as the score for blueberry is higher
User A Strawberries Oranges
Rating 0.8 0.2
Item Strawberries Oranges
Blueberry (B) 1.0 0.3
Tangerine (T) 0 0.9
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Question 2
In user based collaborative filtering, using Jaccard Similarity find the
similarity between two users based on the books that they prefer.
Determine whether users are similar or not.
Users = { User A, User B }
A = { The Hobbit, Harry Potter and the Deathly Hallows , Angels and
Demons }
B = { Angels and Demons, Digital Fortress, The Lost Symbol }
J(A,B) =
= = 0.2 Since the score is nearing zero, users are dissimilar
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Question 3
Using hybrid recommendation( both collaborative and content based filtering) predict the top 3 movie recommendations for user Karen. In the below problem, given is a set of users with a set of their preferred movies belonging to different genres. New User Karen likes Amelie. Based on this data, predict the next 3 recommendations for Karen. Set of Users = {Brian, Ellen, Fred, Dean, Jason}Set of Movies = {Amelie, Star Wars, Hiver, Whispers, Batman, Rambo}Genre = {Action=(Batman, Rambo), Foreign=(Amelie, Hiver, Whispers), Classic=(Star Wars)}
Users Movies
Brian Amelie Star Wars
Ellen Amelie Star Wars Hiver
Fred Star Wars Batman
Dean Star Wars Batman Rambo
Jason Hiver Whispers
Karen ? ? ?
1. Star Wars2. Hiver3. Whispers
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New User Karen likes Amelie.
Based on this data, look for users who like the same movie. Brian and Allen are the two other users who like Amelie. Both of them also
like Star Wars. So Star Wars would be the first movie to be recommended to Karen based on user-item similarity (Collaborative filtering)
User Ellen who likes Amelie and Star Wars also likes Hiver. So Hiver would be the next movie to be recommended to Karen.
After recommending Hiver, now we look at users who like Hiver ( Hiver belongs to genre foreign )
User Jason likes Hiver and Whispers. Hiver and whispers belong to genre – foreign. Now these movies can be matched to user Karen’s original liked movie Amelie ( genre – foreign). Based on content based filtering ( genre)
the next movie recommended to Karen is Whispers.
Thus the top three movie recommendations to user Karen are Star Wars, Hiver and Whispers.
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REFERENCES
• Chengzhi Liu, Caihong Sun and Meiqi Fang, The design of an open hybrid recommendation system for mobile commerce, Communication Technology, 2008. ICCT 2008. 11th IEEE International Conference on E-ISBN: 978-1-4244-2251-7
• Azene Zenebe, Ant Ozok and Anthony F. Norcio, Personalized Recommender Systems in e-commerce and m-commerce:A comparitive Study,11th International Conference on Human-Computer Interaction
• Badrul Sarwar, George Karypis, Joseph Konstan and John Riedl, Analysis of recommendation algorithms for e-commerce, EC '00 Proceedings of the 2nd ACM conference on Electronic commerce, ISBN:1-58113-272-7
• Amund Tveit, Peer to peer based Recommendation for mobile-commerce, ACM Mobile Commerce Workshop,2001