1. مهندسی سيستم‌هاي تجارت الکترونيک
هفته سیزدهم – جلسه مجازی

2. بخش اول پروژه کلاسی

3. اهداف بخش اول پروژه کلاسی
تعريف محدوده اقلام تجاری (کالا و خدمات)
تعيين محدوده اعمال تجاری متناسب با اقلام تجاری
تعيين محدوده فعاليت های تجاری
ترسيم جريان کاری انجام هر يک از فعاليت های تجاری
حاصل از شناخت
حاصل از طراحی

4. کاربرگ محدوده اقلام تجاری

5. کاربرگ اعمال تجاری

6. کاربرگ فعاليت های تجاری

7. کاربرگ جريان های کاری فعاليت های تجاری

8. تحليل سرويس گرا برای سيستم های تجارت الکترونيکی

9. Recommendation Systems in E-Commerce

10. Outline What is Recommendation systems?
Three recommendation approaches
Content-based
Collaborative
Hybrid approach
Conclusions

11. What is Recommendation systems?
Recommendation systems are programs which attempt to predict items that a user may be interested in
Search
Recommendations
Usually, the users rely on search engine to get the information. While, recommendation systems are a useful alternative to search algorithms since they help users discover items they might not have found by themselves.
Items
Products, web sites, blogs, news items, …

12. Recommendation Types Editorial Simple aggregates
Top 10, Most Popular, Recent Uploads
Tailored to individual users
Amazon, Netflix, …
Books, CDs, other products at amazon.com
Movies by Netflix, MovieLens
The most traditional recommendation is from editorial.
Another recommendation is performed by simple aggregate
Now there are some recommendation systems can tailor to individual users.

13. Formal Model C = set of Customers S = set of Items, e.g. books, movies
The space S of possible items and the user space C can be very large.
Utility function u: C £ S ! R
R = set of ratings
R is a totally ordered set
e.g., 0-5 stars, real number in [0,1]
Let C be the set of all users and let S be the set of all possible items that can be recommended, such as books, movies, or restaurants.
The space S of possible items can be very large, ranging in hundreds of thousands or even millions of items in some applications, such as recommendation books or CDs. Similarly, the user space can also be very large – millions in some case.
Let u be a utility function that measures the usefulness of item s to user c, i.e., u : CXS  R, where R is a totally ordered set (e.g., nonnegative integers or real numbers within a certain range).

14. Utility Matrix King Kong LOTR Matrix Nacho Libre Alice Bob Carol David
Here is an example of a user-item rating matrix for a movie recommendation application.
Some of the ratings are empty, which means that the users have not rated the corresponding movies.
In its most common formulation, the recommendation problem is reduced to the problem of estimating ratings for the items that have not been seen by a user.
Carol
David

15. Recommendation Process
Collecting “known” ratings for matrix
Extrapolate unknown ratings from known ratings
Estimate ratings for the items that have not been seen by a user
Recommend the items with the highest estimated ratings to a user
In recommender systems, utility is typically represented by ratings and is initially defined only on the items previously rated by the users. As demonstrated above in the utility Matrix, some of the ratings are empty, which means that the users have not rated the corresponding movies.
Therefore, the recommendation engine should be able to estimate (predict) the ratings of the nonrated movie/user combinations and issue appropriate recommendations based on these predictions.

16. Collecting Ratings Explicit data collection Implicit data collection
Ask people to rate items
Doesn’t work well in practice – people can’t be bothered
Implicit data collection
Learn ratings from user actions
e.g., purchase implies high rating
Examples of explicit data collection include the following:
Asking a user to rate an item on a sliding scale.
Asking a user to rank a collection of items from favorite to least favorite.
Presenting two items to a user and asking him/her to choose the best one.
Asking a user to create a list of items that he/she likes.
Examples of implicit data collection include the following:
Observing the items that a user views in an online store.
Analyzing item/user viewing times[1]
Keeping a record of the items that a user purchases online.
Obtaining a list of items that a user has listened to or watched on his/her computer.

17. Extrapolating Utilities
Key problem: matrix U is sparse
most people have not rated most items
Three approaches
Content-based recommendation
Collaborative recommendation
Hybrid recommendation
Recommender system are usually classified into the following categories, based on how recommendations are made:

18. Content-based recommendations
Main idea: recommend items to customer C similar to previous items rated highly by C
Movie recommendations
recommend movies with same actor(s), director, genre, …
Websites, blogs, news
recommend other sites with “similar” content
According to the previous items rated highly by the same user
For example, in a movie recommendation application, in order to recommend movies to user c, the content-based recommender system tries to understand the commonalities among the movies user c has rated highly in the past (specific actors, directors, genres, subject matter, etc).
Then, only the movies that have a high degree of similarity to whatever the user’s preferences are would be recommended.

19. Plan of action Item profiles User profile likes build recommend Red
Circles
Triangles
match
User profile

20. Item Profiles For each item, create an item profile
Profile is a set of features
movies: author, title, actor, director,…
text: set of “important” words in document
How to pick important words?
Usual heuristic is TF.IDF (Term Frequency times Inverse Doc Frequency)
Item profile is defined with a set of features. For example, in a movie recommendation application, each movie can be represented by its author, title, actor, director, year of release, etc.
One of the best-known measures for specifying keyword weights in Information Retrieval is the Term Frequency/Inverse Document Frequency measure.

21. User profiles and prediction
User profile possibilities:
Weighted average of rated item profiles
Variation: weight by difference from average rating for item …
Traditional heuristic
Given user profile c and item profile s, estimate u(c,s) = cos(c,s)
Need efficient method to find items with high utility
E.g.
After we get the item profiles, how can we build the user profiles?
As stated earlier, content-based systems recommend items similar to those that a user liked in the past
So some average approach, weighted average of rated item profiles can be used to build the user profile
After the user profile is built, one traditional heuristic method, the utility function u(c,s) is usually defined as cosine similarity measure
For example, if user c reads many online articles on the topic of bioinformatics, then content-based recommendation
techniques will be able to recommend other bioinformatics articles to user c.

22. Model-based approaches
For each user, learn a classifier that classifies items into rating classes
liked by user and not liked by user
e.g., Bayesian, regression, SVM
Apply classifier to each item to find recommendation candidates
Problem: scalability
Besides the traditional heuristics that are based mostly on information retrieval methods, other techniques for content-based recommendation have also been used,
such as Bayesian classifiers and various machine learning techniques.

23. Limitations of content-based approach
Finding the appropriate features
e.g., images, movies, music
Overspecialization
Never recommends items outside user’s content profile
People might have multiple interests
Recommendations for new users
How to build a profile?
A new user, having very few ratings, would not be able to get accurate recommendations.
The user has to rate a lot of items before a content-based recommender system can really understand
the user’s preferences and present the user with reliable recommendations. Therefore, a new user, having very few ratings, would not be able to get accurate recommendations.

24. Collaborative Filtering
Consider user c
Find set D of other users whose ratings are “similar” to c’s ratings
Estimate user’s ratings based on ratings of users in D
Set of other users
Similar
Unlike content-based recommendation methods, collaborative recommender systems (or collaborative filtering systems)
try to predict the utility of items for a particular user based on the items previously rated by other users.
For example: in a movie recommendation application, in order to recommend movies to user c, the collaborative recommender system tries to find the “peers” of user c, i.e., other users that have similar tastes in movies (rate the same movies similarly).
Then, only the movies that are most liked by the “peers” of user c would be recommended.
Ratings
Estimate
Ratings

25. Similar users Let rx be the vector of user x’s ratings
Cosine similarity measure
sim(x,y) = cos(rx , ry)
Pearson correlation coefficient
Sxy = items rated by both users x and y
Various approaches have been used to compute the similarity sim(x,y) between users in collaborative recommender systems.
In most of the approaches, the similarity between two users is based on their ratings of items that both users have rated.
The two most popular approaches are cosine and correlation based.
To present them, let …
Note that both the content-based and the collaborative approaches use the same cosine measure from information
retrieval literature. However, in content-based recommender systems, it is used to measure the similarity between
vectors of TF-IDF weights, whereas, in collaborative systems, it measures the similarity between vectors of the actual user-specified ratings.
Sxy is the intersection of sets Sx and Sy.

26. Item-Item Collaborative Filtering
So far: User-user collaborative filtering
Another view
For item s, find other similar items
Estimate rating for item based on ratings for similar items
Can use same similarity metrics and prediction functions as in user-user model
In practice, it has been observed that item-item often works better than user-user
first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on eight real datasets shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.

27. Pros and cons of collaborative filtering
Works for any kind of item
No feature selection needed
New user problem
The same problem as with content-based system
New item problem
Sparsity of rating matrix
they can deal with any kind of content and recommend any items, even the ones that are dissimilar to those seen in the past.
However, collaborative systems have their own limitations.
New user problem: It is the same problem as with content-based systems. In order to make accurate recommendations, the system must first learn the user’s preferences from the ratings that the user gives.
New item problem: New items are added regularly to recommender systems. Collaborative systems rely solely on users’ preferences to make recommendations. Therefore, until the new item is rated by a substantial number of users, the recommender system would not be able to recommend it.
Sparsity of rating matrix: In any recommender system, the number of ratings already obtained is usually very small compared to the number of ratings that need to be predicted.
For example, in the movie recommendation system, there may be many movies that have been rated by only few people
and these movies would be recommended very rarely, even if those few users gave high ratings to them.
One way to overcome the problem of rating sparsity is to use user profile information when calculating user similarity.

28. Hybrid Methods
Implement two separate recommenders and combine their predictions
Add content-based methods to collaborative approach
item profiles for new item problem
deal with sparsity-related problems
Several recommendation systems use a hybrid approach by combining collaborative and content-based methods, which
helps to avoid certain limitations of content-based and collaborative systems
Here is two different ways to combine collaborative and content-based methods:

29. Evaluating Recommendations
Precision
Accuracy of predictions
Compare predictions with known ratings
Recommendation Quality
Top-n measure
Item-Set Coverage
Number of items/users for which system can make predictions

30. Conclusions Content-based Collaborative Hybrid
The user will be recommended items similar to the ones the user preferred in the past
Collaborative
The user will be recommended items that people with similar tastes and preferences liked in the past
Hybrid
Combine collaborative and content-based methods
In this talk, we give an brief introduction about what is the recommendation system, and review three recommendation approaches

31. Combinatorial Auctions

32. Outline Definition Different Approaches
Properties desired from a combinatorial auction mechanism
Bidding Languages
Related works

33. Definition
An auction is a mechanism to allocate a set of goods to a set of bidders on the basis of their bids.
Combinatorial auctions are those auctions in which bidders can place bids on combinations of items, called “packages,” rather than just individual items.

34. 20$
30$ 5$
25$
100$ 1$

35. Other Phrases combinatorial auctions multi-object auctions
Multi goods auctions
package auctions
bundle auctions

36. Classifying auctions into 6 categories of requirements
Resources
Single item or multiple items
Market structure
Forward auction
Reverse auction
Double auctions or Exchange

37. Classifying auctions into 6 categories of requirements
Preference structure
Bid structure
Matching supply to demand
single-sourcing
multi-sourcing
Information feedback
direct mechanism such as a sealed bid auction
indirect mechanism such as an ascending-price auction

38. Different Approaches to Sell a Bundle of Items
Sequential auctions
Parallel auctions
Combinatorial auctions
{A},{B},{C},{A,B},{A,C},{B,C},{A,B,C}
Complementarity
Substitutability

39. Combinatorial Auction (Advantages)
Items may be grouped as bundles
=> Takes into considerations the dependencies between the items.
=> Greater economic efficiency
Allowing bidders more fully to express preferences often leads to improved economic efficiency

40. Example

41. Sets

43. Auction Design- An optimal mechanism
No Bidder is made worse off by participating
Seller Maximum Expected Revenue

44. Properties desired from a combinatorial auction mechanism
Efficiency
value of all the winners is maximized
Individual rationality
every agent gains a non-negative utility by being a participant in the mechanism.
Budget balance
Budget balance ensures that the auctioneer or the exchange does not make losses (positive revenue)

45. Properties desired from a combinatorial auction mechanism
Incentive compatibility
bidding their true valuations for the goods
Solution stability
Revenue maximization or cost minimization
Low transaction costs
No delay
Real-time answer
Efficient determination algorithm
Fairness

46. Bidding languages Atomic bids OR bids XOR bids OR-of-XOR bids
XOR-of-OR bids

47. Other Problems Winner determination problem CAP
Iterative combinatorial auctions
Combinatorial exchanges

48. Related Works Bayesian Combinatorial Auctions
Truthful mechanism design via linear programming
Super solutions for combinatorial auctions
Item pricing for revenue maximization in combinatorial auctions
Combinatorial auction design

49. Some Applications FCC spectrum allocation Electronic procurement
Bandwidth exchanges
Logistics and transportation
Supply chain formation
Distributed resource allocation

50. Conclusions
Combinatorial Auctions can lead to higher economic efficiency
Practical Combinatorial Auctions are hard to implement with compliance to the truth revelation principle

51. Combinatorial Auctions, MIT Press, 2006, Peter Cramton, Yoav Shoham, and Richard Steinberg

52. Chapters
mechanisms, such as the Vickrey auction and the ascending proxy auction
bidding and efficiency issues
computational issues and algorithmic considerations, especially the winner determination problem―how to identify the (tentative) winning set of bids that maximizes revenue
implementation and methods of testing the performance of combinatorial auctions, including simulation and experiment.
four important applications: airport runway access, trucking, bus routes, and industrial procurement

53. References http://www.pnas.org
Introduction to Combinatorial Auctions, Peter Cramton, Yoav Shoham, and Richard Steinberg
Combinatorial auctions for electronic business, Y NARAHARI1 and PANKAJ DAYAMA2