C Porcel et aL / Expert Systems with Applications 36(2009)12520-12528 12525 3.3. Recommendation strategy 2007). In the proposed system we use a triangular function In this phase the system generates the recommendations to de- liver the information to the fitting users. We use the following rec- g(x) 2xfor0≤x≤1/2 ommendation strategies 2-2xfor1/2<x≤1 Next, if the obtained multi-disciplinar value is greater than that of a When a new resource is inserted into the system, it recommends previously defined linguistic threshold ,. the system recommends this information to the users. In this case, the system follows the the complementary resource. To express multi-disciplinar values content-based approach. as a linguistic label in S3, the transformation function in Definition When a new user is inserted into the system, he/she receives 6 is used. Finally, the system sends the resource information and its information about resources, previously inserted, interesting estimated linguistic complementary degree (label of S3)to the for him/her. Now, the system follows the collaborative appropriate user n the following, we describe the process followed when a new user is inserted into the system. a new user gives few information Both the processes are based on a Matching Process among the about the items that satisfied his/her topics of interest, so we use terms used in the users and resources representations(Hanani the collaborative approach to generate the recommendations.We et al, 2001: Korfhage, 1997). We use the vector model (Korfhage, follow a memory-based algorithm, which generates the recom- 1997)to represent both the resource scope and the users topics mendations according to the preferences of nearest neighbors, also of interest. This vector model uses similarity calculations to do known as nearest-neighbor algorithms. These algorithms present the matching process, such as Euclidean Distance or Cosine Mea- good performance as related research reported(Symeonidis, sure. Exactly we use the standard cosine measure(Korthage, Nanopoulos, Papadopoulos, Manolopoulos, 2008). 1997). However, as we have linguistic values, we need to introduce The first step is to identify the users most similar to the new a new linguistic similarity measure ser.using a similarity function. We use the linguistic similarity i(V1,V2) measure o(Vx, Vy) between the topics of interest vectors of the new user(Vx)against all users in the system(Vy, y=1,., n where =4g× kal(4(uik, vik)x4(u2k, a,2k) n is the number of users). If a (Vx, Vy)>8(linguistic threshold va- ∑k1(4-1(nhxn)2xV∑k=1(-1(x,xma)2 lue), the user y is chosen as nearest neighbor of x. Next, the system where g is the granularity of the used term set, n is the number of account the u he resources that satisfied these users and takes into terms used to define the vectors (i.e. the number of disciplines source or not. To obtain the relevance of a resource i for the user x and(vk, M ik)is the 2 nguistic value of term k in th the system aggregates (using the arithmetic mean defined in Def or resource vector(Vi). With this similarity measure we obtain a inition 3)the o(Vx, Vy)with the assessments previously provided about i by the nearest neighbors of x To aggregate the information. linguistic value in S, to assess the similarity among the two re- we need to transform the value on(Vx, Vy) in a linguistic label in Sz sources, two users, or a resource and a user. When a new ce has been inserted into the system, the lin- using the transformation function in Definition 6. guistic similarity measure ai(Vi, V) is computed among the new guistic threshold u, then, the system recommends to the new user esource scope vector(Vi against all the stored resources in the the resource information and its calculated linguistic relevance de- system(Vij=1, ., m where m is the number of resources). If ol(Vi,V)>a(linguistic threshold value to filter out the informa- gree (label of Sz). If not, the system proceeds to estimate if the re- tion). the resource is chosen. Next, the system searches for the is a co for the new user users which were satisfied with these chosen resources(previously they have rated the resource as good )and takes into account the Then the system calculates the linguistic similarity measure user preferences(kind of resources)to consider the user or n (Vx. Vi among the user x and the resource i(for all resources ). To obtain the relevance of the resource i for a selected user x. the Then, it applies the multi-disciplinar function g(x) previously system aggregates (using the arithmetic mean defined in Defini shown( Fig 4)to the value a(Vx, Vi). If the obtained multi-discipli tion 3)the ai(Vi, V) with the assessments previously provided by tem recommends the resource as complementary. To express multi-disciplinar value as a linguistic label in S3, the transform by others users. To aggregate the information we need to trans- tion function in Definition 6 is used ormation function in definition 6 Finally, the system sends to the new users the information of all identified resources that are interesting for them, and its estimate Finally, if the calculated relevance degree is greater than a lin- linguistic complementary degree(label of S3). guistic threshold u, then, the system sends the resource inform on and its calculated linguistic relevance degree(label of S2)to the selected users. If not, the system proceeds to estimate if the re- source could be interesting as a complementary recommendation. To obtain the complementary recommendations, the system calculates the linguistic similarity measure o(Vi, vx)among the resource i and the user x( for all users). Then, it applies a multi-dis- iplinar function to the value o(Vi, V). This function must give greatest weights to similarity middle values (near 0.5 ,because values of total similarity contribute with efficient recommenda- tions but are pre own for the users. Same null values of similarity show a tionship between areas. To establish this function we can centered OWA operators in which the OWA weights are generated from a Gaussian type function (Yager, Fig. 4. Triangular function.3.3. Recommendation strategy In this phase the system generates the recommendations to de￾liver the information to the fitting users. We use the following rec￾ommendation strategies: When a new resource is inserted into the system, it recommends this information to the users. In this case, the system follows the content-based approach. When a new user is inserted into the system, he/she receives information about resources, previously inserted, interesting for him/her. Now, the system follows the collaborative approach. Both the processes are based on a Matching Process among the terms used in the users and resources representations (Hanani et al., 2001; Korfhage, 1997). We use the vector model (Korfhage, 1997) to represent both the resource scope and the users topics of interest. This vector model uses similarity calculations to do the matching process, such as Euclidean Distance or Cosine Mea￾sure. Exactly we use the standard cosine measure (Korfhage, 1997). However, as we have linguistic values, we need to introduce a new linguistic similarity measure: rlðV1; V2Þ ¼ D g  Pn k¼1ðD1 ðv1k; av1kÞ  D1 ðv2k; av2kÞÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn k¼1ðD1 ðv1k; av1kÞÞ2 q  ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn k¼1ðD1 ðv2k; av2kÞÞ2 q 0 B@ 1 CA; where g is the granularity of the used term set, n is the number of terms used to define the vectors (i.e. the number of disciplines) and ðvik; avikÞ is the 2-tuple linguistic value of term k in the user or resource vector ðViÞ. With this similarity measure we obtain a linguistic value in S1 to assess the similarity among the two re￾sources, two users, or a resource and a user. When a new resource has been inserted into the system, the lin￾guistic similarity measure rlðVi; VjÞ is computed among the new resource scope vector ðViÞ against all the stored resources in the system (Vj; j ¼ 1; ... ; m where m is the number of resources). If rlðVi; VjÞ P a (linguistic threshold value to filter out the informa￾tion), the resource j is chosen. Next, the system searches for the users which were satisfied with these chosen resources (previously they have rated the resource as good) and takes into account the user preferences (kind of resources) to consider the user or not. To obtain the relevance of the resource i for a selected user x, the system aggregates (using the arithmetic mean defined in Defini￾tion 3) the rlðVi; VjÞ with the assessments previously provided by x about the similar resources and with the assessments provided by others users. To aggregate the information we need to trans￾form the value rlðVi; VjÞ in a linguistic label in S2, using the trans￾formation function in Definition 6. Finally, if the calculated relevance degree is greater than a lin￾guistic threshold l, then, the system sends the resource informa￾tion and its calculated linguistic relevance degree (label of S2) to the selected users. If not, the system proceeds to estimate if the re￾source could be interesting as a complementary recommendation. To obtain the complementary recommendations, the system calculates the linguistic similarity measure rlðVi; VxÞ among the resource i and the user x (for all users). Then, it applies a multi-dis￾ciplinar function to the value rlðVi; VxÞ. This function must give greatest weights to similarity middle values (near 0.5), because values of total similarity contribute with efficient recommenda￾tions but are probably known for the users. Same, null values of similarity show a null relationship between areas. To establish this function we can use the centered OWA operators in which the OWA weights are generated from a Gaussian type function (Yager, 2007). In the proposed system we use a triangular function (Fig. 4): gðxÞ ¼ 2x for 0 6 x 6 1=2; 2  2x for 1=2 < x 6 1: Next, if the obtained multi-disciplinar value is greater than that of a previously defined linguistic threshold c, the system recommends the complementary resource. To express multi-disciplinar values as a linguistic label in S3, the transformation function in Definition 6 is used. Finally, the system sends the resource information and its estimated linguistic complementary degree (label of S3) to the appropriate users. In the following, we describe the process followed when a new user is inserted into the system. A new user gives few information about the items that satisfied his/her topics of interest, so we use the collaborative approach to generate the recommendations. We follow a memory-based algorithm, which generates the recom￾mendations according to the preferences of nearest neighbors, also known as nearest-neighbor algorithms. These algorithms present good performance as related research reported (Symeonidis, Nanopoulos, Papadopoulos, & Manolopoulos, 2008). The first step is to identify the users most similar to the new user, using a similarity function. We use the linguistic similarity measure rlðVx; VyÞ between the topics of interest vectors of the new user ðVxÞ against all users in the system (Vy; y ¼ 1; ... ; n where n is the number of users). If rlðVx; VyÞ P d (linguistic threshold va￾lue), the user y is chosen as nearest neighbor of x. Next, the system searches for the resources that satisfied these users and takes into account the user preferences (kind of resources) to consider the re￾source or not. To obtain the relevance of a resource i for the user x, the system aggregates (using the arithmetic mean defined in Def￾inition 3) the rlðVx; VyÞ with the assessments previously provided about i by the nearest neighbors of x. To aggregate the information, we need to transform the value rlðVx; VyÞ in a linguistic label in S2, using the transformation function in Definition 6. Finally if the calculated relevance degree is greater than the lin￾guistic threshold l, then, the system recommends to the new user the resource information and its calculated linguistic relevance de￾gree (label of S2). If not, the system proceeds to estimate if the re￾source could be interesting as a complementary recommendation for the new user. Then the system calculates the linguistic similarity measure rlðVx; ViÞ among the user x and the resource i (for all resources). Then, it applies the multi-disciplinar function gðxÞ previously shown (Fig. 4) to the value rlðVx; ViÞ. If the obtained multi-discipli￾nar value is greater than the that of linguistic threshold c, the sys￾tem recommends the resource as complementary. To express multi-disciplinar value as a linguistic label in S3, the transforma￾tion function in Definition 6 is used. Finally, the system sends to the new users the information of all identified resources that are interesting for them, and its estimated linguistic complementary degree (label of S3). 0 0.5 1 1g(x) Fig. 4. Triangular function. C. Porcel et al. / Expert Systems with Applications 36 (2009) 12520–12528 12525