naximum enclosure similarity it has with another conce not in the user profile, but are related to the concepts in the Ci in its corresponding vector Vi. This takes into account user pro the global semantic neighborhood of each concept as follows o calculate the final ranks for each concept, we organize the concepts in a matrix. This is done because we have to if freq(c: in CS,)>0 assign a rank to each concept in the extended user prof max,(ES(Ci, ci)) otherwise for each concept the user has read about. Reading about concept cI increases its value with 1.0. If concept c2 is di- rectly related to concept cl, then its value is increased with ES(ci, ci) N(c)∩N(c) IN( (11) 0.5. If there is a concept, concept c], in the extended pro- file which is neither equal to concept ci nor is it related to Finally the similarity between ti and ti is computed using concept ci, its value is decreased with 0. 1. These constant he coil were determined by experimenting with values ranging from 0 to l with a step of O.1. Al a matrix with rank values. The columns contain the item SemRel(ti, ti)=cos(Vi, Vi) ∈0,1,(12) e extended user profile (UR) and the rows contain ns from the user profile (0). Table 1 shows a rank where the nominator is the dot product of both vectors and d ui eu. summing the values he denominator is the multiplication of the magnitude of of the cells in a column of the matrix, and repeating this each vector process for each column,results a vector with the final The advantage of this approach above concept equiva- ranks for each concept, in the extended user profile lence, binary cosine, and Jaccard, is that it also takes into ccount the related concepts of a concept that occurs in a Table 1: Rank matrix 3.5 Ranked semantic recommendation 5 describes an intuitive approach in working with adap- tive hypermedia. For instance when you read something about concept ci which is related to concept c2 and concept ou increase not only your knowledge in concept ci but m| Im2 also in the other two concepts Even though it is used in a different research field(adap- The user might have read one or more articles abo tive hypermedia), the main idea can be applied also here. cept. Logically, the user is presumed to be more in Each concept is assigned a value, this value we call the rank. in concepts that are found in several articles. The For example, when a user reads about Google, he might also of articles the user has read about concept ui, is called the interested in its competitors, like Yahoo but also in news weight wi about its CEO. Eric Schmidt. Both are considered to be in direct relation to the concept Google. Therefore we increase the rank for Google, Yahoo!, and Eric Schmidt. Unrelated W={un,u2,…,m} oncepts, i. e, concepts that are not directly connected to Now we can calculate the value for each cell in the above he current concept, also need to be addressed. This means, matrix. This is done as follows if a user profile consists of concepts ci and c2, and the next article the user reads, contains concept c, which is directly related to cI, but not related to c2. we increase the rank +1.0ife;=u f ci, and decrease the rank of c2. By decreasing the rank r=t;×+0.5ie;≠u,e∈r(u for such a concept we make the user profile adaptive to the -0.1 otherwise users main interest The final rank for each concept from the extended user pro- Che set of related keywords to concept ci is defined as file, can be computed by taking the sum of the values of the corresponding column in the matrix: r(ci R is described as the union of all related concepts to the Rank(e)=∑r (18) the user profile Those sums are stored in a vector Vu. Each concept in R=∪r(a (14) the extended user profile now has a rank. Before we can And finally UR is defined as the set of all concepts and cor- d to ensure that the range of the ranks is [o, 1 ]. The responding related concepts, this is called the extended user normalization is done as follows profile UREUUR The extended user profile is used in order to be able to in- where v E Vu and Wu E Vu. With this normalization we crease the interest of the user in certain concepts that are can compare the extended user profile to a new article thatmaximum enclosure similarity it has with another concept cj in its corresponding vector Vj . This takes into account the global semantic neighborhood of each concept as follows: wi =  1 if freq(ci in CSj ) > 0 maxj (ES(ci, cj )) otherwise (10) where ES(ci, cj ) = |N(ci) ∩ N(cj )| |N(ci)| . (11) Finally the similarity between ti and tj is computed using the cosine measure: SemRel(ti, tj ) = cos(Vi, Vj ) = Vi · Vj ||Vi|| · ||Vj || ∈ [0, 1] , (12) where the nominator is the dot product of both vectors and the denominator is the multiplication of the magnitude of each vector. The advantage of this approach above concept equiva￾lence, binary cosine, and Jaccard, is that it also takes into account the related concepts of a concept that occurs in a text. 3.5 Ranked Semantic Recommendation [5] describes an intuitive approach in working with adap￾tive hypermedia. For instance when you read something about concept c1 which is related to concept c2 and concept c3 you increase not only your knowledge in concept c1 but also in the other two concepts. Even though it is used in a different research field (adap￾tive hypermedia), the main idea can be applied also here. Each concept is assigned a value, this value we call the rank. For example, when a user reads about Google, he might also be interested in its competitors, like Yahoo!, but also in news about its CEO, Eric Schmidt. Both are considered to be in direct relation to the concept Google. Therefore we increase the rank for Google, Yahoo!, and Eric Schmidt. Unrelated concepts, i.e., concepts that are not directly connected to the current concept, also need to be addressed. This means, if a user profile consists of concepts c1 and c2, and the next article the user reads, contains concept c3, which is directly related to c1, but not related to c2, we increase the rank of c1, and decrease the rank of c2. By decreasing the rank for such a concept we make the user profile adaptive to the user’s main interest. The set of related keywords to concept ci is defined as: r(ci) = n c i 1, c i 2, · · · , c i k o . (13) R is described as the union of all related concepts to the concepts in the user profile: R = [ ui∈U r(ui) . (14) And finally UR is defined as the set of all concepts and cor￾responding related concepts, this is called the extended user profile: UR = U ∪ R . (15) The extended user profile is used in order to be able to in￾crease the interest of the user in certain concepts that are not in the user profile, but are related to the concepts in the user profile. To calculate the final ranks for each concept, we organize the concepts in a matrix. This is done because we have to assign a rank to each concept in the extended user profile for each concept the user has read about. Reading about concept c1 increases its value with 1.0. If concept c2 is di￾rectly related to concept c1, then its value is increased with 0.5. If there is a concept, concept c3, in the extended pro- file which is neither equal to concept c1 nor is it related to concept c1, its value is decreased with 0.1. These constants were determined by experimenting with values ranging from 0 to 1 with a step of 0.1. Applying this procedure results in a matrix with rank values. The columns contain the items from the extended user profile (UR) and the rows contain the items from the user profile (U). Table 1 shows a rank matrix, where ei ∈ UR and ui ∈ U. Summing the values of the cells in a column of the matrix, and repeating this process for each column, results in a vector with the final ranks for each concept, in the extended user profile. Table 1: Rank matrix e1 e2 . . . eq u1 r11 r12 . . . r11 u2 r21 r22 . . . r2q . . . . . . . . . . . . . . . um rm1 rm2 . . . rmq The user might have read one or more articles about a con￾cept. Logically, the user is presumed to be more interested in concepts that are found in several articles. The number of articles the user has read about concept ui, is called the weight wi, W = {w1, w2, · · · , wm} . (16) Now we can calculate the value for each cell in the above matrix. This is done as follows: ri,j = wi × 8 < : +1.0 if ej = ui +0.5 if ej 6= ui, ej ∈ r(ui) −0.1 otherwise . (17) The final rank for each concept from the extended user pro- file, can be computed by taking the sum of the values of the corresponding column in the matrix: Rank(ej ) = Xm i=1 rij . (18) Those sums are stored in a vector VU . Each concept in the extended user profile now has a rank. Before we can compare the user profile with an unread news article, we need to ensure that the range of the ranks is [0,1]. The normalization is done as follows: VU [vi] = vi − min(vu) max(vu) − min(vu) , (19) where vi ∈ VU and vu ∈ VU . With this normalization we can compare the extended user profile to a new article that