Recommendation Systems in Mobile Commerce Presented by Rachana Chandrashekar( )

Your company slogan 1. INTRODUCTION 2. OVERVIEW 3. RECOMMENDER SYSTEM MODEL OUTLINE 4. RECOMMENDATION ALGORITHMS

Your company slogan 6. CHALLENGES IN MOBILE COMMERCE 7. CONCLUSION 5. CHALLENGES OF RECOMMENDER SYSTEMS OUTLINE

Your company slogan 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 Only 10% of products on an online retail store garner 75% of page views

Your company slogan 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

Your company slogan 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

Your company slogan EVERYDAY EXAMPLES OF RECOMMENDATION SYSTEMS..

Your company slogan MORE EXAMPLES.. Netflix predicts other “Movies You Love” based on past numeric ratings (1-5 stars) Recommendations drive 60% of Netflix’s DVD rentals

Your company slogan MORE EXAMPLES..

Your company slogan MORE EXAMPLES..

Your company slogan 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

Your company slogan RECOMMENDATION ALGORITHMS  Collaborative filtering (CF) Hypothesis : Similar users tend to like similar items Hypothesis : Similar users tend to like similar items Two forms of CF Two forms of CF -Item-based collaborative filtering -User-based collaborative filtering  Data Collection Methods -Explicit feedback Example: ratings, dismiss Example: ratings, dismiss -Implicit feedback Example: number of views, purchases Example: number of views, purchases

Your company slogan DATA REPRESENTATION  Items : i 1, i 2, i 3 …. i n  User u 1,u 2,u 3 ….u n has provided ratings on items Example of User/Movie Ratings Matrix: Example of User/Movie Ratings Matrix: AliceBobCharlieDave Harry Potter …3523 American Pie442- Twilight …51-- Matrix-115

Your company slogan A NAÏVE RECOMMENDATION SYSTEM

Your company slogan Rating provided by user u for item j Similarity between items i and j ITEM-BASED COLLABORATIVE FILTERING

Your company slogan EXAMPLE : ITEM-BASED CF  Given user with ratings for items X and Y  Items N and S with similarities score(u,N) = 1.0* *0.3 = 0.89 score(u,N) = 1.0* *0.3 = 0.89 score(u,S) = 0.2* *0.8 = 0.4 score(u,S) = 0.2* *0.8 = 0.4 Harry Potter (X)The Matrix (Y) rating Item Harry Potter (X)The Matrix (Y) The Chronicles of Narnia (N) Star Wars (S)0.20.8

Your company slogan COMPUTING SIMILARITY BETWEEN ITEMS Cosine Similarity - Items are represented as u-dimensional vectors over user space - Items are represented as u-dimensional vectors over user space - Similarity is cosine of the angle between two vectors - Similarity is cosine of the angle between two vectors - Score ranges between 1 (perfect) and -1 (opposite) - Score ranges between 1 (perfect) and -1 (opposite)

Your company slogan Example: 2 users Example: 2 users ItemsUser 1User 2 A B C 0.3

Your company slogan JACCARD SIMILARITY MEASURE

Your company slogan USER BASED COLLABORATIVE FILTERING  K – nearest neighbors ( KNN ) -Group users into different clusters  Hard clustering  Soft clustering Users Clusters Items

Your company slogan CONTENT-BASED RECOMMENDATION Collaborative filtering does not require any information about the items - However, it might be reasonable to exploit such information - However, it might be reasonable to exploit such information -E.g. Recommend fantasy novels to people who liked fantasy novels in the past 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

Your company slogan CONTENT REPRESENTATION AND ITEM SIMILARITIES

Your company slogan HYBRID RECOMMENDER SYSTEMS Combination of collaborative filtering and content based filtering Combination of collaborative filtering and content based filtering Idea of crossing two or more implementations Idea of crossing two or more implementations Hybrid features Hybrid features - Social Features - Social Features Movies liked by user Movies liked by user - Content features - Content features Dramas liked by user Dramas liked by user - Hybrid features - Hybrid features User who like many movies that are dramas User who like many movies that are dramas

Your company slogan 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 - No historical information for new users or itemsScalability - Increase in the size of matrix - Increase in the size of matrix

Your company slogan CHALLENGES IN DESIGNING RECOMMENDER SYSTEMS FOR MOBILE USERS Size of the display, small screen devices Size of the display, small screen devices Limited input and interaction capabilities Limited input and interaction capabilities Mobile users have shorter browsing sessions Mobile users have shorter browsing sessions Lack of standardization of the browsing tools Lack of standardization of the browsing tools Cost of interaction Cost of interaction Exclusive characteristics : Location awareness Location awareness Ubiquity Ubiquity

Your company slogan 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

Your company slogan 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* *0.3 = 0.86 score (u,B) = 0.8* *0.3 = 0.86 score(u,T) = 0.8* *0.9 = 0.18 score(u,T) = 0.8* *0.9 = 0.18 The item blueberry is recommended to the user as the score for blueberry is higher User A Strawberries Oranges Rating ItemStrawberriesOranges Blueberry (B) Tangerine (T)00.9

Your company slogan Question 2

Your company slogan 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 BrianAmelieStar Wars EllenAmelieStar WarsHiver FredStar WarsBatman DeanStar WarsBatmanRambo JasonHiverWhispers Karen??? 1.Star Wars 2.Hiver 3.Whispers

Your company slogan 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. the next movie recommended to Karen is Whispers. Thus the top three movie recommendations to user Karen are Star Wars, Hiver and Whispers.

Your company slogan REFERENCES Chengzhi Liu, Caihong Sun and Meiqi Fang, The design of an open hybrid recommendation system for mobile commerce, Communication Technology, ICCT th IEEE International Conference on E-ISBN: Chengzhi Liu, Caihong Sun and Meiqi Fang, The design of an open hybrid recommendation system for mobile commerce, Communication Technology, ICCT th IEEE International Conference on E-ISBN: Azene Zenebe, Ant Ozok and Anthony F. Norcio, Personalized Recommender Systems in e- commerce and m-commerce:A comparitive Study,11 th International Conference on Human- Computer Interaction Azene Zenebe, Ant Ozok and Anthony F. Norcio, Personalized Recommender Systems in e- commerce and m-commerce:A comparitive Study,11 th 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: 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: Amund Tveit, Peer to peer based Recommendation for mobile-commerce, ACM Mobile Commerce Workshop,2001 Amund Tveit, Peer to peer based Recommendation for mobile-commerce, ACM Mobile Commerce Workshop,2001