1. Toward the Next generation of Recommender systems
IEEE Transactions on Knowledge and Data Engineering
Volume 17 , Issue 6 (June 2005)
Written by Gediminas Adomavicius, Alexander Tuzhilin
Summarized by Gihyun Gong

2. About paper This paper is about an overview of recommendation system
Focused on rating based recommendation which is most popular
Content based
Collaborative filtering
Hybrid methods
Extending capabilities of recommendation system

3. Outline About recommendation Recommendation methods
Demographic filtering
Content-based Methods
Collaborative Methods
Hybrid Methods
Current research issues in recommendation system

4. Recommendation
Recommendation is type of information filtering technique that attempts to present information items (movies, music, books, news, images, web pages) that are likely of interest to the user
Recommendation can be formulated as :
C : all users
S : set of all possible item
u : function that measures the usefulness of item s to user c
Recommendation is reduced to the problem of estimating ratings for the items that have not been seen by a user
How to rating?
How to estimating?
Rating : which indicates how a particular user liked a particular item
Profile : includes various user characteristics (such as age…)
Item space S : defined with a set of charateristics

5. Recommendation (cont’d)
Problem of recommender system
Usually not defined on the whole C X S space, but only on some subset of it
Recommendation engine should be able to estimate the ratings of the non-rated movie/user

6. Recommendation system
Recommendation system is a system which has the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options
Recommender systems are usually classified into the following categories, based on how recommendations are made:
Demographic filtering
Content-based recommendations: The user will be recommended items similar to the ones the user preferred in the past
Collaborative recommendations: The user will be recommended items that are preferred by other people with similar tastes and preferences
Hybrid approaches: These methods combine collaborative and content-based methods.

7. Demographic filtering
Uses demographic information
Ages, Jobs, Location, …
Advantages
No feedback is needed
No cold start problem
Disadvantages
Can not provide personalization
Low accuracy
Too general

8. Content-based recommendation
Recommend items similar to those users preferred in the past
User preference profile is the key
Matching “user preferences” with “item characteristics”
Designed mostly to recommended text-based items
The content in these system is usually described with keywords
Similarity measure
TF-IDF
Cosine similarity
사용자의 내부에서 오는 추천

9. Similarity function TF-IDF Cosine Similarity
N is the number of documents
Ni is How many times keyword ki is appears in the document
Fi,j is the number of times keyword ki is appears in the document j
Cosine Similarity
For text matching, the attribute vectors A and B are usually the tf-idf vectors of the documents. v1
user v2

10. Limitation of Content-based method
Limited Content Analysis
This method is based on text, but not all content is well represented by keywords
Picture, Taste, …
Overspecialization
User is limited to being recommended items already rated
Unrated items not shown
Use random or mutation in genetic algorithm to solve
New User Problem
This method uses user preference profile
New user have very few ratings (or no history available)
System needs new user’s rating of sample items
However, people usually do not want to rate sample items

11. Collaborative Filtering
Using Trend information, 『Word of Mouth』
Basic idea of CF
Build a ratings table from user rating.
Compare user’s ratings, and calculate similarity between users. We call the user group which presents high similarity that ‘Nearest Neighborhood’
Predict user preference based on rating of Nearest neighborhood.

12. Collaborative Filtering methods
Memory-based (or Nearest-Neighborhood)
Similarity based model
Use entire collection of previously rate item by the user
Store all user information in a Database
Model-based
Probabilistic model
Use collection of rating to learn a model, which is used to make rating prediction
Based on machine-learning
Bayesian network, Clustering, NN, …

13. Advantages of Collaborative Filtering
Can deal with multimedia contents
Can recommend based on user preference and quality of item
Can recommend serendipity item

14. Limitation of Collaborative method
New User Problem
Must first learn the user’s preferences from the ratings that the user gives
New Item Problem
Until the new item is rated by a substantial number of users, the recommender system would not be able to recommend it
User’s rating problem
Different users might use different scales
Sparsity
The number of ratings already obtained is usually very small compared to the number of ratings that need to be predicted
Scalability
Computing cost grows with C X S space
System typically have to search millions of users and items, it causes a serious scalability problem
However, these correlations will change when new users are added
Adaptability
Requirement of a user may change over time

15. Surveys on Hybrid method
Combining separate recommender
Linear combination of two outputs
Voting scheme
Adding Content-based to Collaborative model
Add Content-based profile for each user
Use filterbot, the virtual user
Adding Collaborative to Content-based model
Add user profiles presented by term vector for each items
Single unifying model
Knowledge-based techniques
Entrée uses some domain knowledge
Quickstep, Foxtrot system uses topic ontology

16. Extending capabilities
Comprehensive understanding of Users and Items
Profiles in pure content-based and collaborative-based still tend to be quite simple and do not utilize some of the more advanced profiling techniques
In addition to using traditional profile features, such as keywords and simple user demographics more advanced profiling techniques based on data mining rules, sequences, and signatures that describe a user’s interests can be used to build user profiles

17. Extending capabilities (cont’d)
Multidimensionality of Recommendations
Current recommendation system uses only 2-dimension
User x Item
We can extend dimension of recommendation
Context(TPOK), Demographic information, …

18. Extending capabilities (cont’d)
Example of multidimension : The movie
Traditional recommendation consider just 2 space
Who is the user?
What movie?
We can consider other information
Characteristics of the movie?
Person wants to see movie?
Where and how the movie will be seen?
With whom the movie will be seen?
When will the movie be seen?

19. Extending capabilities (cont’d)
Multicriteria Rating
To expand rating criteria
Taking a linear combination of multiple criteria and reducing the problem to a single-criterion optimization problem
Optimizing the most important criterion and converting other criteria to constraint

20. Extending capabilities (cont’d)
Restaurant example :

21. Extending capabilities (cont’d)
Nonintrusiveness
The problem of feedback normalizing
One way to explore the intrusiveness problem is to determine an optimal number of ratings the system should ask from a new user
This topic is related to Opinion Mining

22. Extending capabilities (cont’d)
Flexibility
Most of the recommendation methods are “hard-wired” into the systems
Therefore, the end-user cannot customize recommendations according to his or her needs in real time.
Also, most of the recommender systems recommend only individual items to individual users and do not deal with aggregation.
However, it is important to be able to provide aggregated recommendations in a number of applications, such as recommend brands or categories of products to certain segments of users (e.g. Vacations in Florida - Students).
One way to support aggregated recommendations is by utilizing the OLAP-based approach.
Recommendation Query Language (RQL)

23. Extending capabilities (cont’d)
RQL is SQL-like language for expressing flexible user-specified recommendation requests
“recommend to each user from New York the best three movies that are longer than two hours” can be expressed in RQL”.