Two algorithm for anti-spam

1. Anti-spam Algorithm TrustRank 、 Hilltop 954203041 林裕得 954203057 蔡繼正

8. Compare with Page Rank 、 Trust Rank 、 Hilltop(3/3) 0.16 0.16 0.16 0.16 0.16 0.16 0.33 0 0.33 0 0.33 0 0.5 0.2 0.3 Algorithm 1 or 0 Average Initial Score Expert pages All pages All pages Inlinks Source Hilltop Trust Rank Page Rank

12. U S M 1 N …………… . M

26. Computing the Expert Score(1/2) S 0 ：包含 k 個 ( 所有 )keywords 的 Key Phrase 的總值 S 1 ：包含 k-1 個 keywords 的 Key Phrase 的總值 S 2 ：包含 k-2 個 keywords 的 Key Phrase 的總值 S i = SUM (key phrases p with k-i query terms) LevelScore(p) * FullnessFactor(p,q) LevelScore ： 16 of title, 6 of heading, 1 of anchor m is the number of terms in p which are not in q If m <= 2, FullnessFactor(p,q) = 1 If m > 2, FullnessFactor(p,q) = 1 – (m-2) / plen Query: A B S 0 = 16*1 S 1 = 16*1 + 6*1 + 16*1 S 2 = 0 Title: A B C H1: A

27. Computing the Expert Score(2/2) S 0 ：包含 k 個 ( 所有 )keywords 的 Key Phrase 的總值 S 1 ：包含 k-1 個 keywords 的 Key Phrase 的總值 S 2 ：包含 k-2 個 keywords 的 Key Phrase 的總值 Expert_Score = ( 2 32 * S 0 ) + ( 2 16 * S 1 ) + S 2

29. Computing the Target Score(1/2) occ(w,T) is the number of distinct key phrases in E that contain w and qualify the edge(E,T) If occ(w,T) is 0 for any query keyword then the Edge_Score(E,T) = 0 Otherwise, Edge_Score(E,T) = Expert_Score(E) * SUM (query keywords w) occ(w,T) T E edge

30. Computing the Target Score(2/2) Target_Score = SUM ( non-affiliated E) Edge_Score(E,T) T E 1 E 2 E 3 E2 and E3 are affiliated, and ES(E 2 ,T) > ES(E 3 ,T)

31. Evaluation-Trust Rank(1/3)

37. Q&A