2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 龙星计划课程 : 信息检索 Next-Generation Search Engines ChengXiang Zhai ( 翟成祥 ) Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology, Statistics University of Illinois, Urbana-Champaign

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Outline Overview of web search Next generation search engines

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Characteristics of Web Information “Infinite” size (Surface vs. deep Web) –Surface = static HTML pages –Deep = dynamically generated HTML pages (DB) Semi-structured –Structured = HTML tags, hyperlinks, etc –Unstructured = Text Different format (pdf, word, ps, …) Multi-media (Textual, audio, images, …) High variances in quality (Many junks) “Universal” coverage (can be about any content)

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, General Challenges in Web Information Management Handling the size of the Web –How to ensure completeness of coverage? –Efficiency issues Dealing with or tolerating errors and low quality information Addressing the dynamics of the Web –Some pages may disappear permanently –New pages are constantly created

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, “Free text” vs. “Structured text” So far, we’ve assumed “free text” –Document = word sequence –Query = word sequence –Collection = a set of documents –Minimal structure … But, we may have structures on text (e.g., title, hyperlinks) –Can we exploit the structures in retrieval?

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Examples of Document Structures Intra-doc structures (=relations of components) –Natural components: title, author, abstract, sections, references, … –Annotations: named entities, subtopics, markups, … Inter-doc structures (=relations between documents) –Topic hierarchy –Hyperlinks/citations (hypertext)

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Structured Text Collection... Subtopic 1 Subtopic k A general topic General question: How do we search such a collection?

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Exploiting Intra-document Structures [Ogilvie & Callan 2003] Title Abstract Body-Part1 Body-Part2 … D D1D1 D2D2 D3D3 DkDk Intuitively, we want to combine all the parts, but give more weights to some parts Think about query-likelihood model… “part selection” prob. Serves as weight for D j Can be trained using EM Select D j and generate a query word using D j Anchor text can be treated as a “part” of a document

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Exploiting Inter-document Structures Document collection has links (e.g., Web, citations of literature) Query: text query Results: ranked list of documents Challenge: how to exploit links to improve ranking?

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Exploiting Inter-Document Links Description (“anchor text”) Hub Authority “Extra text”/summary for a doc Links indicate the utility of a doc What does a link tell us?

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, PageRank: Capturing Page “Popularity” [Page & Brin 98] Intuitions –Links are like citations in literature –A page that is cited often can be expected to be more useful in general PageRank is essentially “citation counting”, but improves over simple counting –Consider “indirect citations” (being cited by a highly cited paper counts a lot…) –Smoothing of citations (every page is assumed to have a non- zero citation count) PageRank can also be interpreted as random surfing (thus capturing popularity)

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, The PageRank Algorithm (Page et al. 98) d1d1 d2d2 d4d4 “Transition matrix” d3d3 Iterate until converge N= # pages Stationary (“stable”) distribution, so we ignore time Random surfing model: At any page, With prob. , randomly jumping to a page With prob. (1-  ), randomly picking a link to follow. I ij = 1/N Initial value p(d)=1/N

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, PageRank in Practice Interpretation of the damping factor  (  0.15): –Probability of a random jump –Smoothing the transition matrix (avoid zero’s) Normalization doesn’t affect ranking, leading to some variants The zero-outlink problem: p(di)’s don’t sum to 1 –One possible solution = page-specific damping factor (  =1.0 for a page with no outlink)

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, HITS: Capturing Authorities & Hubs [Kleinberg 98] Intuitions –Pages that are widely cited are good authorities –Pages that cite many other pages are good hubs The key idea of HITS –Good authorities are cited by good hubs –Good hubs point to good authorities –Iterative reinforcement…

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, The HITS Algorithm [Kleinberg 98] d1d1 d2d2 d4d4 “Adjacency matrix” d3d3 Initial values: a(d i )=h(d i )=1 Iterate Normalize:

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Basic Search Engine Technologies Cached pages Crawler Web ---- … ---- … ---- … Indexer (Inverted) Index … Retriever Browser Query Host Info. Results User Efficiency!!! Coverage Freshness Precision Error/spam handling

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Component I: Crawler/Spider/Robot Building a “toy crawler” is easy –Start with a set of “seed pages” in a priority queue –Fetch pages from the web –Parse fetched pages for hyperlinks; add them to the queue –Follow the hyperlinks in the queue A real crawler is much more complicated… –Robustness (server failure, trap, etc.) –Crawling courtesy (server load balance, robot exclusion, etc.) –Handling file types (images, PDF files, etc.) –URL extensions (cgi script, internal references, etc.) –Recognize redundant pages (identical and duplicates) –Discover “hidden” URLs (e.g., truncated) Crawling strategy is a main research topic (i.e., which page to visit next?)

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Major Crawling Strategies Breadth-First (most common(?); balance server load) Parallel crawling Focused crawling –Targeting at a subset of pages (e.g., all pages about “automobiles” ) –Typically given a query Incremental/repeated crawling –Can learn from the past experience –Probabilistic models are possible The Major challenge remains to maintain “freshness” and good coverage with minimum resource overhead

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Component II: Indexer Standard IR techniques are the basis –Basic indexing decisions (stop words, stemming, numbers, special symbols) –Indexing efficiency (space and time) –Updating Additional challenges –Recognize spams/junks –Exploit multiple features (PageRank, font information, structures, etc) –How to support “fast summary generation”? Google’s contributions: –Google file system: distributed file system –Big Table: column-based database –MapReduce: Software framework for parallel computation –Hadoop: Open source implementation of MapReduce (mainly by Yahoo!)

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Google’s Basic Solutions URL Queue/List Cached source pages (compressed) Inverted index Hypertext structure Use many features, e.g. font, layout,…

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Component III: Retriever Standard IR models are applicable but insufficient –Different information need (home page finding vs. topic-driven) –Documents have additional information (hyperlinks, markups, URL) –Information is often redundant and the quality varies a lot –Server-side feedback is often not feasible Major extensions –Exploiting links (anchor text, link-based scoring) –Exploiting layout/markups (font, title field, etc.) –Spelling correction –Spam filtering –Redundancy elimination In general, rely on machine learning to combine all kinds of features

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Effective Web Retrieval Heuristics High accuracy in home page finding can be achieved by –Matching query with the title –Matching query with the anchor text –Plus URL-based or link-based scoring (e.g. PageRank) Imposing a conjunctive (“and”) interpretation of the query is often appropriate –Queries are generally very short (all words are necessary) –The size of the Web makes it likely that at least a page would match all the query words Combine multiple features using machine learning

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Home/Entry Page Finding Evaluation Results (TREC 2001) MRR%top10%fail Unigram Query Likelihood + Link/URL prior i.e., p(Q|D) p(D) [Kraaij et al. SIGIR 2002] Exploiting anchor text, structure or links Query example: Haas Business School

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Named Page Finding Evaluation Results (TREC 2002) Dirichlet Prior + Title, Anchor Text (Lemur) [Ogilvie & Callan SIGIR 2003] Okapi/BM25 + Anchor Text Best content-only Query example: America’s century farms

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Learning Retrieval Functions Basic idea: –Given a query-doc pair (Q,D), define various kinds of features Fi(Q,D) –Examples of feature: the number of overlapping terms, p(Q|D), PageRank of D, p(Q|Di), where Di may be anchor text or big font text –Hypothesize p(R=1|Q,D)=s(F1(Q,D),…,Fn(Q,D), ) where is parameters –Learn by fitting function s with training data (i.e., (d,q)’s where d is known to be relevant or non-relevant to q) Methods: –Early work: logistic regression [Cooper 92, Gey 94] –Recent work: Ranking SVM [Joachims 02], RankNet [Burges et al. 05]

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Learning to Rank Advantages –May combine multiple features (helps improve accuracy and combat web spams) –May re-use all the past relevance judgments (self- improving) Problems –No much guidance on feature generation (rely on traditional retrieval models) All current Web search engines use some kind of learning algorithms to combine many features

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Next-Generation Search Engines

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Limitations of the Current Search Engines Limited query language –Syntactic querying (sense disambiguation?) –Can’t express multiple search criteria (readability?) Limited understanding of document contents –Bag of words & keyword matching (sense disambiguation?) Heuristic query-document matching: mostly TF-IDF weighting –No guarantee for optimality –Machine learning can combine many features, but content matching remains the most important component in scoring

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Limitations of the Current Search Engines (cont.) Lack of user/context modeling –Using the same query, different users would get the same results (poor user modeling) –The same user may use the same query to find different information at different times Inadequate support for multiple-mode information access –Passive search support: A user must take initiative (no recommendation) –Static navigation support: No dynamically generated links –Consider more integration of search, recommendation, and navigation Lack of interaction Lack of task support

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Towards Next-Generation Search Engines Better support for query formulation –Allow querying from any task context –Query by examples –Automatic query generation (recommendation) Better search accuracy –More accurate information need understanding (more personalization and context modeling) –More accurate document content understanding (more powerful content analysis, sense disambiguation, sentiment analysis, …) More complex retrieval criteria –Consider multiple utility aspects of information items (e.g., readability, quality, communication cost) –Consider collective value of information items (context-sensitive ranking)

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Towards Next-Generation Search Engines (cont.) Better result presentation –Better organization of search results to facilitate navigation –Better summarization More effective and robust retrieval models –Automatic parameter tuning More interactive search More task support

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Looking Ahead…. More user modeling –Personalized search –Community search engine (collaborative information access) More content analysis and domain modeling –Vertical search engines –More in-depth (domain-specific) natural language understanding –Text mining More accurate retrieval models (life-time learning) Going beyond search –Towards full-fledge information access: integration of search, recommendation, and navigation –Towards task support: putting information access in the context of a task

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Summary Web provides many challenges and opportunities for text information management Search engine technology  crawling + retrieval models + machine learning + software engineering Current generation of search engines are limited in user modeling, content understanding, retrieval model… Next generation of search engines likely moves toward personalization, domain-specific vertical search engines, collaborative search, task support, …

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, What You Should Know Special characteristics of Web information (as compared with ordinary text collection) Two kinds of structures of a text collection (intra-doc and inter-doc) Basic ideas of PageRank and HITS How a web search engine works Limitations of current search engines