Social Computing and Its Application in Query Suggestion

Transcription

1 Social Computing and Its Application in Query Suggestion Irwin King Department of Computer Science & Engineering The Chinese University of Hong Kong

2 Billionaires Shuffle 2007 William Gates Warren Buffett 2008 Warren Buffett Mark Zuckerberg Facebook in Carlos Slim Helu & family Carlos Slim Helu & family 2008 at 23 and $1.5 billion later... William Gates

3 Alexa as of Nov USA CHINA Global Google Baidu Yahoo Yahoo QQ Google Myspace Sina YouTube YouTube Google.cn Windows Live Facebook Taobao Facebook Windows Live 163 MSN MSN Yahoo Myspace Wikipedia Google Wikipedia EBay Sohu Blogger AOL Youku Yahoo.jp

4 What s On the Menu? Web 2.0 and Social X Social Computing Some Interesting Problems Collaborative Filtering Query Suggestion

5 What s On the Menu? Web 2.0 and Social X Social Computing Some Interesting Problems Collaborative Filtering Query Suggestion

6 Web 2.0 Web as a medium vs. Web as a platform Read-Only Web vs. Read-and-Write Web Static vs. Dynamic Restrictive vs. Freedom & Empowerment Technology-centric vs. User-centric Limited vs. Rich User Experience Individualistic vs. Group/Collective Behavior Consumer vs. Producer Transactional vs. Relational Top-down vs. Bottom-up People-to-Machine vs. People-to-People Search & browse vs. Publish & Subscribe Closed application vs. Service-oriented Services Functionality vs. Utility Data vs. Value

7 Social Networking

8 Social Search Social Search Engine Leveraging your social networks for searching

9 Social Bookmarking

10 Social Media

11 Social News/Mash Up

12 Social Knowledge Sharing

13 Social Marketing Viral marketing Who are the brokers? Who can exert the most influence on buying/ selling? How much should one advertise?

14 Social/Human Computation

15 Human Computation

16 Web 2.0 Revolution The Three C s Connectivity Collaboration Communities

17 What s On the Menu? Web 2.0 and Social X Social Computing Some Interesting Problems Collaborative Filtering Query Suggestion

18 Social Relations presence crew identity teams populations binary social role squad cardinal reputation organizations cohorts markets communities partners groups integer real expertise trust ownership accountability knowledge

19 social network services blogs s wikis instant messaging mobile devices social bookmarking Social Computing Social Behavior ranking Regression tagging Intelligent Computation Clustering Algorithms Classification security & privacy NLP Collective Intelligence Model Selection Theory collaborative filtering social marketing human computation opinion mining/ sentiment analysis query logs large graph analysis algorithms

20 Social Computing (SC) Social computing is a general term for an area of computer science that is concerned with the intersection of social behavior and computational systems. Wikipedia A social structure in which technology puts power in communities, not institutions. Forrester Forms of web services where the value is created by the collective contributions of a user population.

21 Issues Theory and models Seach, mining, and ranking of existing information, e.g., spatial (relations) and temporal (time) domains Dealing with partial and incomplete information, e.g., collaborative filtering, ranking, tagging, etc. Scalability and algorithmic issues Security and privacy issues Monetization of social interactions

22 Machine Learning in SC Classification, clustering, regression, etc. New insights on the data Social relations are often hidden (latent) Change data from (x, y) to (x, c 1 (x),c 2 (x),,y) c(x) = context in tags, relations, ratings, etc. data type = binary, integer, real, cardinal, etc.

23 Organizational Chart

24 Social Network Chart Authority vs. Importance

25 What s On the Menu? Web 2.0 and Social X Social Computing Some Interesting Problems Collaborative Filtering Query Suggestion

26 What s On the Menu? Web 2.0 and Social X Social Computing Some Interesting Problems Collaborative Filtering Query Suggestion

27 A Better Mousetrap?

28 Challenges Queries contain ambiguous and new terms apple: apple computer or apple pie? NDCG:? Users tend to submit short queries consisting of only one or two words almost 20% one-word queries almost 30% two-word queries Users may have little or even no knowledge about the topic they are searching for!

29 What is Clickthrough Data Query logs recorded by search engines u, q, l, r, t Users relevance feedback to indicate desired/preferred/target results

30 Joint Bipartite Graph B uq =(V uq,e uq ) V uq = U Q U = {u 1,u 2,..., u m } Q = {q 1,q 2,..., q n } E uq = {(u i,q j ) there is an edge from u i to q j } is the set of all edges. The edge (u i,q j ) exists in this bipartite graph if and only if a user u i issued a query q j. B ql =(V ql,e ql ) V ql = Q L Q = {q 1,q 2,..., q n } L = {l 1,l 2,..., l p } E ql = {(q i,l j ) there is an edge from q i to l j } is the set of all edges. The edge (q j,l k ) exists if and only if a user u i clicked a URL l k after issuing an query q j.

31 Key Points Two-level latent semantic analysis Level 1 Level 2 { { Consider the use of a joint user-query and query-url bipartite graphs for query suggestion Use matrix factorization for learning query features in constructing the Query Similarity Graph Use heat diffusion for similarity propagation for query suggestions

32 Users Queries URLs 0.8 J I G H C 0.8 D F A Bipartite Graphs 0.8 E B Query Similarity Graph Queries are issued by the users, and which URLs to click are also decided by the users Two distinct users are similar if they issued similar queries Two queries are similar if they are issued by similar users

33 rij s jk U i Q j L k Normalized weight, how many times u i issued q j Normalized weight, how many times q j is linked to l k L-dimensional vector of user u i L-dimensional vector of query q j L-dimensional vector of URL l k H(R, U, Q) = min U,Q H(S, Q, L) = min Q,L 1 2 m i=1 n j=1 + α u 2 U 2 F + α q 2 Q 2 F 1 2 n j=1 p k=1 + α q 2 Q 2 F + α l 2 L 2 F I R ij(r ij g(u T i Q j )) 2 I S jk(s jk g(q T j L k )) 2

34 H(S, R, U, Q, L) = 1 n p I S 2 jk(s jk g(q T j L k )) 2 + α r 2 j=1 k=1 + α u 2 U 2 F + α q 2 Q 2 F + α l 2 L 2 F, A local minimum can be found by performing gradient descent in Ui, Qj and Lk m i=1 n Iij(r R ij g(u i T Q j )) 2 j=1

35 Gradient Descent Equations H U i H Q j = H L k = = α r p k=1 n j=1 + α r m n j=1 I R ijg (U T i Q j )(g(u T i Q j ) r ij)q j + α u U i, I S jkg (Q T j L k )(g(q T j L k ) s jk)l k i=1 I R ijg (U T i Q j )(g(u T i Q j ) r ij)u i + α q Q j, I S jkg (Q T j L k )(g(q T j L k ) s jk)q j + α l L k, Only the Q matrix, the queries latent features, is being used to generate the query similarity graph!

36 Query Similarity Graph 0.8 J I G H C 0.8 D 0.7 F E 0.8 Similarities are calculated using queries latent features Only the top-k similar neighbors (terms) are kept 0.3 B 0.1 A 0.2 k = 4

37 Similarity Propagation Based on the Heat Diffusion Model In the query graph, given the heat sources and the initial heat values, start the heat diffusion process and perform P steps Return the Top-N queries in terms of highest heat values for query suggestions

38 Heat Diffusion Model Heat diffusion is a physical phenomena ρc P T t = Q + (k T ) Heat flows from high temperature to low temperature in a medium Heat kernel is used to describe the amount of heat that one point receives from another point The way that heat diffuse varies when the underlying geometry ρ C P T t Q k T v Density Heat capacity and constant pressure Change in temperature over time Heat added Thermal conductivity Temperature gradient Divergence

39 Heat Diffusion Process 0.8 J I G H C 0.8 D F A E 0.8 B

40 Similarity Propagation Model f i (t + t) f i (t) t α H ij = τ i d i f i (t) = f(1) = e αr f(0), k:(q i,q k ) E w ik + j:(q j,q i ) E f(1) = e αh f(0) w ji f j (t) d j w ji /d j, (q j,q i ) E, (τ i /d i ) k:(i,k) E w ik, i = j, 0, otherwise. R = γh + (1 γ)g1 T (1) (2) (3) (4) α d i f i (t) w ik f(0) f(1) τ i γ g Thermal conductivity Heat value of node i at time t Heat value of node i at time t Weight between node i and node k Vector of the initial heat distribution Vector of the heat distribution at time 1 Equal to 1 if node i has outlinks, else equal to 0 Random jump parameter, and set to 0.85 Uniform stochastic distribution vector

41 Discrete Approximation Compute e αr is time consuming We use the discrete approximation to substitute f(1) = ( I + α ) P P R f(0) For every heat source, only diffuse heat to its neighbors within P steps In our experiments, P = 3 already generates fairly good results

42 Query Suggestion Procedure For a given query q 1. Select a set of n queries, each of which contains at least one word in common with q, as heat sources 2. Calculate the initial heat values by fˆqi (0) = W(q) W(ˆq i) W(q) W(ˆq i ) q = Sony Sony = 1 Sony Electronics = 1/2 Sony Vaio Laptop = 1/3 3. Use f(1) = e αr f(0) to diffuse the heat in graph 4. Obtain the Top-N queries from f(1)

43 Physical Meaning of α If set α to a large value The results depend more on the query graph, and more semantically related to original queries, e.g., travel => lowest air fare If set α to a small value The results depend more on the initial heat distributions, and more literally similar to original queries, e.g., travel => travel insurance

44 Experimental Dataset Data Source Collection Period Lines of Logs Unique user IDS Unique queries Unique URLs Unique words Clickthrough data from AOL search engine March 2006 to May 2006 (3 months) 19,442,629 After Pre- Processing 657, ,371 4,802, ,165 1,606, ,302 69,937

45 Query Suggestions

46 Comparisons ODP, Open Directory Project, see

47 Impact of Parameter k To test the extend of similarity needed

48 Impact of Parameter P To test the propagation influence

49 Efficiency Analysis

50 Summary Propose an offline novel joint matrix factorization method using user-query and query-url bipartite graphs for learning query features Propose an online diffusion-based similarity propagation and ranking method for query suggestion

51 Conclusion Social Computing is a paradigm shift! Novel views on the spatial and temporal relationship among social entities! Great opportunities in a new research direction!

52 On-Going Research Machine Learning Direct Zero-norm Optimization for Feature Selection (ICDM 08) Semi-supervised Learning from General Unlabeled Data (ICDM 08) Learning with Consistency between Inductive Functions and Kernels (NIPS 08) An Extended Level Method for Efficient Multiple Kernel Learning (NIPS 08) Semi-supervised Text Categorization by Active Search (CIKM 08) Transductive Support Vector Machine (NIPS 07) Global and local learning (ICML 04, JMLR 04) Web Intelligence Effective Latent Space Graph-based Re-ranking Model with Global Consistency (WSDM 09) Formal Models for Expert Finding on DBLP Bibliography Data (ICDM 08) Learning Latent Semantic Relations from Query Logs for Query Suggestion (CIKM 08) RATE: a Review of Reviewers in a Manuscript Review Process (WI 08) MatchSim: link-based web page similarity measurements (WI 07) Diffusion rank: Ranking web pages based on heat diffusion equations (SIGIR 07) Web text classification (WWW 07) Collaborative Filtering Recommender system: accurate recommendation based on sparse matrix (SIGIR 07) SoRec: Social Recommendation Using Probabilistic Matrix Factorization (CIKM 08) Human Computation An Analytical Study of Puzzle Selection Strategies for the ESP Game (WI 08) An Analytical Approach to Optimizing The Utility of ESP Games (WI 08)

53 Acknowledgments Prof. Michael R. Lyu Prof. Jimmy Lee Dr. Kaizhu Huang Dr. Haixuan Yang Dr. Zenglin Xu (Ph.D.) Hongbo Deng (Ph.D.) Zhenjiang Lin (Ph.D.) Hao Ma (Ph.D.) Haiqin Yang (Ph.D.) Xin Xin (Ph.D.) Thomas Chan (M.Phil) Chao Zhou (Ph.D.)

54 VeriGuide Similarity text detection system Developed at CUHK Promote and uphold academic honesty, integrity, and quality Support English, Traditional and Simplified Chinese Handle.doc,.txt,.pdf,.html, etc. file formats Generate detailed originality report including readability

55 Theory and modeling of social computing in education Technology and software of social computing for education Social educational system design and architectures Case studies, best practices, and demos of social media in education Benchmark and experiments on social computing in education Mobile learning applications for social computing Semantic web standards for e-learning Software for social learning and collaborative learning Life long social learning network Quality and reliability of information and resources Privacy, risk and security issues in education using social media Virtual space for leaning communities Ubiquitous, distributed, and collaborative learning Integration of social learning spaces Social computing in education trend analysis Web 2.0 and social computing for learning (media sharing, media manipulation, conversational arenas, online games, virtual worlds, social networking, blogging, social bookmarking, recommender systems, collaborative editing, wikis, syndication, etc.)

56 Q & A