1 / 31

Recommendation Systems in Mobile Commerce

Recommendation Systems in Mobile Commerce. Presented by Rachana Chandrashekar(7487187) rchan092@uottawa.ca. OUTLINE. 1. INTRODUCTION . 2 . OVERVIEW . 3 . RECOMMENDER SYSTEM MODEL . 4 . RECOMMENDATION ALGORITHMS . OUTLINE. 5. CHALLENGES OF RECOMMENDER SYSTEMS .

gitano
Download Presentation

Recommendation Systems in Mobile Commerce

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Recommendation Systems in Mobile Commerce Presented by Rachana Chandrashekar(7487187) rchan092@uottawa.ca

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

  3. OUTLINE 5. CHALLENGES OF RECOMMENDER SYSTEMS 6. CHALLENGES IN MOBILE COMMERCE 7. CONCLUSION

  4. 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 INTRODUCTION The Paradox of Choice

  5. 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

  6. 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 WHY DO WE NEED RECOMMENDATION SYSTEMS?

  7. EVERYDAY EXAMPLES OF RECOMMENDATION SYSTEMS..

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

  9. MORE EXAMPLES..

  10. MORE EXAMPLES..

  11. RECOMMENDER SYSTEM MODEL Items Users Candidate Generation Automatically identify items of interest to users(Focus of talk) Filtering Filters: near duplicates, already seen, dismissed Recommendations based on temporal, geo-location and personalization Rank Track user feedback, likes, dislike, ratings User Feedback

  12. 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 RECOMMENDATION ALGORITHMS

  13. DATA REPRESENTATION • Items : i1, i2, i3 …. in • User u1,u2,u3….un has provided ratings on items Example of User/Movie Ratings Matrix:

  14. A NAÏVE RECOMMENDATION SYSTEM Aggregate ratings for each item Recommend item with maximum rating score(i,u) = f(i) = Does everybody like Harry Potter movies? Historical information about users is important!

  15. ITEM-BASED COLLABORATIVE FILTERING • 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

  16. 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

  17. 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)

  18. Example: 2 users

  19. 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

  20. USER BASED COLLABORATIVE FILTERING • K – nearest neighbors ( KNN ) • Group users into different clusters • Hard clustering • Soft clustering Users Items Clusters

  21. 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

  22. CONTENT REPRESENTATION AND ITEM SIMILARITIES

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. Question 2 Inuser 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

  29. 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)} Star Wars Hiver Whispers

  30. 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.

  31. 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 • AzeneZenebe, Ant Ozok and Anthony F. Norcio, Personalized Recommender Systems in e-commerce and m-commerce:Acomparitive Study,11th International Conference on Human-Computer Interaction • BadrulSarwar, 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 • AmundTveit, Peer to peer based Recommendation for mobile-commerce, ACM Mobile Commerce Workshop,2001

More Related