Web Search. 2 o Semestre 2012/2013

Transcription

1 Dados na Dados na Departamento de Engenharia Informática Instituto Superior Técnico 2 o Semestre 2012/2013

2 Bibliography Dados na Bing Liu, Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 2nd edition. Chapter 6, Sections 6.8,6.9,6.10

3 Outline Dados na 1 2 3

4 Dados na 1 Crawling (later) 2 Index construction 3 Searching and Ranking

5 Index construction Dados na we just saw it: parallel sort-merge ot hit-lists trick: document order follows document importance, independently of query

6 ing and Ranking Dados na preprocessing query terms (stemming, stop-word removal, etc) finding pages containing the query terms ranking pages and returning them to the user as there are many pages matching each possible query and quality is very heterogeneous, authoritative information must be used to assess the quality of web pages. Ranking functions evaluate both the reputation and the content of each page. PageRank will be discussed later.

7 Content-based Evaluation Dados na Considers, for each term, its Occurence type Count Position Occurence types may be: Title Anchor text URL Body Similarity: Compute the dot product of the type weight vector and the count weight vector of each page, yelding the IR score of the page.

8 Combining Ranks Dados na many features are included in a web similarity formula: IR similarity (query-independent) reputation score many other, driven by business-oriented criteria

9 Outline Dados na 1 2 3

10 Principle Dados na

11 Combining Similarity Scores Dados na 1 eliminate duplicates 2 apply a fusion algorithm using similarity scores provided by underlying SE not. these techniques can be used also to combine ranking functions within a search engine

12 Combination Using Similarities Dados na CombMIN(d j ) = min(s 1j, s 2j,..., s kj ) (use the minimum ranking) CombMAX (d j ) = max(s 1j, s 2j,..., s kj ) CombSUM(d j ) = s ij (add the similarity scores) CombMNZ(d j ) = CombSUM(d j ) r j, where r j is the number of systems that retrieved d j CombSUM and CombMNZ perform better. CombMNZ slighlty outperforms CombSUM in most cases.

13 Combination using ranking positions Dados na Borda(1770) Ranking: each voter assigns a linear preference order of candidates, n to the first, n 1 to the second, etc. Unranked candidates divide the votes. Winner gets the most points. Condorcet (1787) Ranking: do pairwise comparisons to count how many times a doc wins, loses or ties against other documents (as in a soccer tournament). Doc with most wins gets highest score. Ties broken on number of losses. Reciprocal ranking: assign a score 1/pos to each doc. Rank based on sum of scores.

14 : Borda rankings example Dados na 5 underlying search engines, which have ranked four candidate pages a, b, c, d. System 1: System 2: System 3: System 4: System 5: a,b,c,d b,a,d,c c,b,a,d c,b,d c,b Scores: Score(a) = = 11.5 Score(b) = = 16 Score(c) = = 15 Score(d) = = 7.5 The final ranking is: b, c, a, d

15 : Condorcet rankings example Dados na System 1: System 2: System 3: System 4: System 5: a,b,c,d b,a,d,c c,b,a,d c,b,d c,b comparisons (win:lose:tie): pair a b c d a - 1:4:0 2:3:0 3:1:1 b 4:1:0-2:3:0 5:0:0 c 3:2:0 3:2:0-4:1:0 d 1:3:1 0:5:0 1:4:0 - win lose tie a b c d The final ranking is: c, b, a, d

16 : MRR example Dados na 5 underlying search engines, which have ranked four candidate pages a, b, c, d. System 1: System 2: System 3: System 4: System 5: a,b,c,d b,a,d,c c,b,a,d c,b,d c,b Scores: Score(a) = 1 + 1/2 + 1/ = 1.83 Score(b) = 1/ /2 + 1/2 + 1/2 = 3 Score(c) = 1/3 + 1/ = 3.55 Score(d) = 1/4 + 1/3 + 1/4 + 1/3 + 0 = 1.17 The final ranking is: c, b, a, d

17 Outline Dados na 1 2 3

18 Dados na Activity of deliberately misleading a search engine by a website owner. Deceivers try to understand how a ranking function computes, by changing the ranking of a page without changing its user-perceived value. SEO - Search Engine Optimization: A business activity that sometimes is legitimate, but often is not perceived as ethical.

19 dumping of many unrelated terms Tom Cruise Content Dados na Attempt to affect the TF.IDF based ranking features Places where to add spam terms: Title meta-tags body anchor text URL Techniques repeat some important terms the picture mining quality of the camera mining is amazing

20 Link Dados na out-link spamming easy: pick popular websites from directories in-link spamming Create a honey pot Add links to web directories Post links to user-generated content sites Participate in a link exchange Create a spam farm

21 Hiding Techniques Dados na Content hiding: pick background white and font color also white Cloaking: serve one page to normal clients and another to search engines Redirection: redirect browser to another page (user sees one, search engine will crawl both)

22 Combating Spam Dados na Give higher weight to anchor text PageRank - assign authority to pages based on number and importance of links TrustRank - the good guys and the bad guys cluster together learn from language features common in spam (longer titles, longer words,...) partition pages in blocks and compute PageRank on a block basis (instead of assigning a single PR value to each page), to defeat honeycombs and link exchanges...an on-going process...

23 Dados na Questions?