1. FlexRecs: Expressing and Combining Flexible Recommendations IDS Lab. Seminar Winter 2010 Minsuk Kahng minsuk@europa.snu.ac.kr Jan. 8 th, 2010 G. Koutrika, B. Bercovitz, H. Garcia-Molina SIGMOD 2009 Center for E-Business Technology Seoul National University Seoul, Korea Stanford InfoLab Intelligent Database Systems Lab. 강 민 석강 민 석

2. Copyright  2010 by CEBT Abstract  FlexRecs Recommendation systems have a number of limitations. Algorithm is hard-wired into the system. propose a recommendation framework, FlexRecs decouple the definition of a recommendation process declaratively define recommendation process as a high-level workflow comprise traditional relational operators and new operators Prototype, flexible recommendation engine Realize the proposed framework, FlexRecs 2

3. Contents  Introduction  Related Work  Recommendation Framework  System Architecture  Experiments  Conclusions 3

4. Copyright  2010 by CEBT Introduction  Recommendation System Provide advices on movies, products, travel, and many other topics Become very popular in systems Google News, Amazon, MovieLens Many recommendation approaches have been proposed. 4

5. Copyright  2010 by CEBT Motivation  CourseRank Stanford InfoLab has developed CourseRank A social tool that helps students to make informed choices about classes 수업 공식 정보, 수업 게시판, 학점 분포, 수강 후기, 시간표, 4 년 시간표, 추천 등 5

6. Copyright  2010 by CEBT Motivation  Challenges The need for flexibility and expressivity Initial version offered no choices 추천 결과는 n 개의 list 만 제공 추천된 n 개 중 1 개와 관련된 더 많은 추천 결과를 보고 싶어도 방법이 없음. type 을 제한하기, 친구의 이력을 기반으로 추천하기, 학점이 비슷한 사람의 추천 등 불가 The need for experimentation and higher productivity 여러 추천 기법의 통합 환경에 따라 방법 X 와 방법 Y 중 좋은 경우 다름 두 추천 방법에 적절한 weight 을 줘서 결합할 필요성 여러 추천 기법의 구현 time-consuming, counter-productive not easily expandable and manageable 6

7. Copyright  2010 by CEBT Introduction  Limitations of Recommendation Hard Wired NOT expressed declaratively – algorithm typically embedded in the system code Make it hard to modify the algorithms, or experiment with different approaches No Flexibility 추천 결과는 fixed. End users are given few choices Users may expect diverse recommendations in different contexts. Unable to request recommendations for user-defined constraints Limited World Model 일반적으로 추천은 deal with two types entities: users & items Provide recommendations using richer data representations is not straightforward. 7

8. Copyright  2010 by CEBT Introduction  FlexRecs, Proposed Framework Flexible Recommendations to be easily defined, customized, processed over structured data Decouples definition of recommendation process Declaratively define recommendation process as a high-level workflow Enable generating any recommendations with the same engine Recommendation expressed as a high-level workflow contain traditional relational operators plus new recommendation operators can handle data in relational form Designers can create multiple, customizable workflows Prototype flexible recommendation engine that realizes the framework Execute a workflow over conventional DBMS 8

9. Copyright  2010 by CEBT Contents  Introduction  Related Work  Recommendation Framework  System Architecture  Experiments  Conclusions 9

10. Copyright  2010 by CEBT Related Work  Limitations of Recommendation Systems Algorithms are hard wired in the system code. Design, implement, experiment with new methods can be time-consuming. Generate only a predefined and fixed set of recommendations 기존 방법 ( 컨텐츠 기반, CF) 의 문제점 해결하기 위한 여러 시도들 과거 이력에 지나치게 의존하는 문제, cold-start problem in CF 등 But, may be required under different circumstances by different users Limited World 많은 실제 app. 에서 reside much richer data in DB. Different types of entities may co-exist in a single DB. Current ones are not very expressive  Some extensions Incorporate multi-criteria ratings into recommendations Language RQL Allow users to formulate recommendation in a flexible manner But, not very expressive because formulated on a pre-specified multi-D cube of ratings 10

11. Contents  Introduction  Related Work  Recommendation Framework Data Model Operators Recommendation Workflow  System Architecture  Experiments  Conclusions 11

12. Copyright  2010 by CEBT Data Model  Data Model Data reside in structured form, and particularly in relational form. Focus on databases that follow relational model  Base Relation Database comprises a set of relations. A Relation has a set of attributes An attribute instantiated to a single value is called base attribute. A relation with only base attributes called base relation. 12

13. Copyright  2010 by CEBT Data Model  Extended Relation The authors introduce the concept of an extended relation. Now, an attribute value can be a relation. 13

14. Copyright  2010 by CEBT Data Model  Extended Relation Examples can be thought as “views” Generalized? Model and Language could be generalized to arbitrary nesting No need for generality for practical scenarios Materialized or not? This issue if orthogonal to their definition. may not be stored in DB 14

15. Copyright  2010 by CEBT Operators  Base Operators can operate on base and extended relations Operators Select select tuples from relation, for which the condition holds condition refers only to base attributes 결과는 base or extended relation depending on 원래 type Project project the relation into a smaller set of its attributes A is a list of base, embedded or extended attributes Join combine tuples in two relations that meet some condition condition refers only to base attributes about Nested Relation Algebra Such generality is not necessary for practical recommendation. 15

16. Copyright  2010 by CEBT Operators  The Extend Operator information that conceptually refers to entity is found in several relations. create extended attributes in the tuples of a relation Example Ratings made by each student as a single “unit of information” per student 16

17. Copyright  2010 by CEBT The Recommend Operator  Comparison function Recommendations are based on comparisons e.g. Courses are rated by comparing their topics to student’s interests. e.g. User-User similarity in CF Have a library of comparison functions for recommendation tasks Comparison Function P 에는 기본적으로 attribute 가 들어갈 수 있음. 17

18. Copyright  2010 by CEBT The Recommend Operator  Comparison function Examples Comparisons of string values – Jaccard similarity Comparisons of numerical values – Simple Distance Using conditional probabilities Comparisons of extended values Comparisons of single values to extended values 18

19. Copyright  2010 by CEBT The Recommend Operator  Aggregation Comparison function Comparison functions compare one tuple to another tuple. Desirable to compare one tuple to a set of tuples Combine all partial values into a final one (e.g. max, avg) Example Weighted average of the partial comparison values 19

20. Copyright  2010 by CEBT The Recommend Operator  Recommend Operator Score value of each tuple is produced by comparing it to other tuples R i 의 tuple r i 을 R j 의 모든 tuple 와 함수 cf 을 이용하여 비교한 후 aggregation 함수 a 을 이용해서 그 결과를 aggregate 한 결과가 value v 추천 후보인 R i 의 tuple r i 각각에 대해 점수 값을 얻게 됨. Example Alice 에게 course 을 추천 20

21. Copyright  2010 by CEBT The Blend Operator  Blend Combine recommendations generated by two different processing paths e.g. 친구들이 들은 과목 기반 추천 + 졸업을 위해 필요한 과목 기반 추천 Blending methods Occurrence-based blending Normalized blending Weighted average blending 21

22. Copyright  2010 by CEBT Recommendation Workflows  Recommendation and Blend Operators capture the essence of most recommendation approaches can be composed and combined with select, project, join to describe rec.  Recommendation Workflow Examples take several examples 가상의 학생 (user) Alice 가 요청 당연한 몇 가지 사항들은 제외 Alice 가 이미 소비한 item 들은 제외하기 22

23. Copyright  2010 by CEBT Recommendation Workflows  Recommendation Workflow Examples Example 1 : Related Courses Alice 는 현재 “Programming: Part One”(C22) 과목에 대해 보는 상태 2008 년에 제공되는 과목 중 이 과목과 비슷한 과목을 추천하기 비교 함수로는 과목명 (Title) 에 대해 Jaccard Similarity 를 이용 23 CourseIDTitleScore C23Programming: Part Two2/4 = 0.5 C25Advanced Programming Methodology 1/5 = 0.2 C30Computer Graphics0/5 = 0 ………

24. Copyright  2010 by CEBT Recommendation Workflows  Recommendation Workflow Examples Example 2 : Content-based Recommendation Alice(StudID=1234) 는 literature, writing 관련 과목들을 이미 수강한 상태 올해 (2008 년 ) 들을 과목을 그 동안 Alice 가 들었던 과목과 비슷하게 추천 받고자 함 24

25. Copyright  2010 by CEBT Recommendation Workflows  Recommendation Workflow Examples Example 3 : Nearest-neighbor collaborative filtering SuID=444 인 학생과 비슷한 취향의 학생을 찾아서 이들의 이력을 기반으로 추천 비슷한 취향의 학생의 점수를 많이 반영하여 각 과목에 대한 점수 도출 Course is rated by taking weighted average of the ratings provided by these students. Comparisons of single values to extended values 25

26. Copyright  2010 by CEBT Recommendation Workflows  Recommendation Workflow Examples Example 5 : Blending Ex.2 에서 구한 content-based 결과와 Ex.3 에서 구한 CF 결과를 blend 0.7 :1 의 비율로 반영 26

27. Copyright  2010 by CEBT Recommendation Workflows  Recommendation Workflow Examples Ex. Many recommend and blend operators 과목 내용이 비슷한 학생, 학점 (GPA) 가 비슷한 학생 모두 고려하여 추천 Ex. Classification Alice 가 Honor Student 들과 얼마나 비슷한지 판단하여 Honor Student 여부 판단 Ex. Recommending a major Course 외의 다른 item(major) 도 추천 가능 Ex. Item-to-item movie recommendation Item based CF 27

28. Contents  Introduction  Related Work  Recommendation Framework  System Architecture Architecture Recommendation Plan Generator  Experiments  Conclusions 28

29. Copyright  2010 by CEBT System Architecture  Architecture Workflow Manager allow designer to define rec. workflows Hide details Workflow Parser Construct an expression tree Recommendation Plan Generator Generate a rec. execution plan Plan is a sequence of SQL and func. calls Recommendation Generator Execute a plan and returns the rec. Send SQL to DB engine 29

30. Copyright  2010 by CEBT Recommendation Plan Generator  Recommendation Plan Generation Build a recommendation plan by traversing an expression tree Query 1 – similar users (create temporary in-memory table) Query 2 & 3 – One Recommendation (Example 3) Query 4 - Blend 30

31. Contents  Introduction  Related Work  Recommendation Framework  System Architecture  Experiments  Conclusions 31

32. Copyright  2010 by CEBT Experiments  Objective Examine the feasibility and performance of flexible recommendations Study different workflows with different characteristics real data 사용 written in Java on top of MySQL  Workflow Collaborative Filtering Major recommendation Related courses Friends-of-friends more complex that content-based and CF ones 32

33. Copyright  2010 by CEBT Experiments  Workflow Collaborative Filtering 모든 user 에 대해 다른 모든 user 와 similarity 구해서 추천할 때, user 별 평균 시간 Gen time 은 SQL 생성 시간으로 수행 시간에 비해 얼마 걸리지 않음 User 수 증가에 따라 선형적으로 증가 comparison function 어떤 것을 쓰더라도 비슷한 결과  Summary easy to create multiple workflows and execute them transparently over the same flexible rec. system that combines extensibility with reasonable performance 33

34. Contents  Introduction  Related Work  Recommendation Framework  System Architecture  Experiments  Conclusions 34

35. Copyright  2010 by CEBT Conclusions  Contributions decouple the definition of a recommendation process Introduce an extend operator that generates a virtual nested relation define recommend & blend operators that capture essence of rec. workflows provide several examples that show how common rec. can be expressed describe a prototype flexible recommendation engine that realizes the proposed framework New operators can be compiled into standard SQL for execution. present experimental results that show the potential of FlexRecs  Future Work make possible to study the optimization of multiple recommendation workflows currently work on scaling over very large inputs Automatically balance complexity and effectiveness and identify the best option It would be interesting to define flexible rec. for XML or ontologies. design appropriate user interfaces for enabling users express flexible rec. 35

36. Copyright  2010 by CEBT Discussion  Flexible make “flexible” Synergy  Decouple the Definition of Recommendation Recommend operator 로 generalize  use Nested Relation Nested Relational Model 을 이렇게 이용 실제로는 GROUP BY 쓰면 될 일  SQL use conventional DBMS 지금도 SQL 을 이용한 추천 구현이 가능한데, 성능 평가가 필요한지 36

37. 37  Thank you~