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value of each rule. However, it is not easy to deal with other B characteristics, as the novelty and significance of the paper to the user may change over time To deal with the characteristics(2) and (3), we utilized the user's paper viewing history. This allowed us to check whether or not a paper is novel. Moreover, this monitoring enabled us to also determine a user's preference and interest and check whether or not a paper is of interest at the present moment. Additionally, we define thetopic model that has two elements: the topic frequency and the topic recency. By using the word co-occurrence in a database we measure the opic frequency. Also, by using the Jaccard coefficient, we measure the topic recency In the first section we will describe the recommendation algorithm for managing research papers. Second, we will outline out the Papits research support system. Third, we will discuss the our results using our algorithm and proves Figure 1. A conception of scale-free network ts usefulness. Fourth, we will compare our work with ilar researches. Finally, we will conclude with a brief 2. Paper recommendation mechanism mo). The probability that a vertex attaches to another ver- tex i is proportional to the rate an edge hi which a vertex i When constructing the user s model, we used the scale- has3J[] free network to measure the frequency and recency of words. The scale-free network has the characteristics of growthand preferential attachment. We also use'fit I1=I(k)≡ =7-1+mo (0≤i<r+mo)(1) ness of vertices in the network. The fitness is the probabil ity when a new vertex is added to the network. So, when constructing a network, we look upon papers which a user The probability P(k)that a vertex in the network inter- possesses as a user's interests and specialities. We repre- acts with k other vertices decays as a power law, following sented the words contained in papers that a user possesses P(k) as the vertices in the network and the word co-occurrence The collaboration graph of movie actors represents a as the edges in network. well-documented example of a social network. Each tor is represented by a vertex, two actors being connected if 2. 1. Scale-free network they were cast together in the same movie. The probability that an actor has k links(characterizing his or her popu- larity) has a power-law tail for large k, following P(k) The scale-free network results from two generic mecha- actor, where 2.3+0.1. A more complex net nisms:(i) networks continuously expand with the addition work with over 800 million vertices is the www, where a of new vertices, and ( ii) new vertices preferentially attach to vertex is a document and the edges are the links pointing edges that are already well connected. The scale -free net from one document to another. The topology of this graph work concept is as follows determines the Web's connectivity and, consequently, our (i)iniTially,thenetworkhasnoedgesandmoverticeseffectivenessinlocatinginformationonthewww.Infor (Figure 1 A) In Figure 1, a o means an added vertex, a. mation about P(k)can be obtained using robots, indicating means the existing vertices. The network grow sequentially at the probability that k documents point to a certain Web from A to B, and from B to C in Figure I every step?. The page follows a power law, with y, 2.1±0.1[3 added vertex attaches the existing m vertices. The added Real networks have a competitive aspect, as each node vertex attaches the existing m vertices and these processes has an intrinsic ability to compete for edges at the expense are called'growth'in the scale-free network. of other vertices[10]. They propose a model in which each (ii)As the network adds new vertices(i= y+ mo), the node is assigned a fitness parameter ni which does not vertices preferentially select a vertex which already well change in time. Thus at every time step a new node j with a connected from the existing vertices(i=0,1,.,y-1+ fitness n; is added to the system, where n, is chosen from a Proceedings of the 2005 International Workshop on Data Engineering Issues in E-Commerce(DEEC'05) 076952401-X0520.00@2005LEEE SOCIETYvalue of each rule. However, it is not easy to deal with other characteristics, as the novelty and significance of the paper to the user may change over time. To deal with the characteristics(2) and (3), we utilized the user’s paper viewing history. This allowed us to check whether or not a paper is novel. Moreover, this monitoring enabled us to also determine a user’s preference and interest and check whether or not a paper is of interest at the present moment. Additionally, we define the ‘topic model’ that has two elements: the topic frequency and the topic recency. By using the word co-occurrence in a database, we measure the topic frequency. Also, by using the Jaccard coefficient, we measure the topic recency. In the first section, we will describe the recommendation algorithm for managing research papers. Second, we will outline out the Papits research support system. Third, we will discuss the our results using our algorithm and proves its usefulness. Fourth, we will compare our work with similar researches. Finally, we will conclude with a brief summary. 2. Paper recommendation mechanism When constructing the user’s model, we used the scalefree network to measure the frequency and recency of words. The scale-free network has the characteristics of ‘growth’ and ‘preferential attachment’. We also use ‘fitness’ of vertices in the network. The fitness is the probability when a new vertex is added to the network. So, when constructing a network, we look upon papers which a user possesses as a user’s interests and specialities. We represented the words contained in papers that a user possesses as the vertices in the network, and the word co-occurrence as the edges in network. 2.1. Scale-free network The scale-free network results from two generic mechanisms: (i) networks continuously expand with the addition of new vertices, and (ii) new vertices preferentially attach to edges that are already well connected. The scale-free network concept is as follows: (i)Initially, the network has no edges and m0 vertices (Figure 1 A). In Figure 1, a ◦ means an added vertex, a • means the existing vertices. The network grow sequentially from A to B, and from B to C in Figure 1 every step γ. The added vertex attaches the existing m vertices. The added vertex attaches the existing m vertices and these processes are called ‘growth’ in the scale-free network. (ii)As the network adds new vertices(i = γ + m0), the vertices preferentially select a vertex which already well connected from the existing vertices(i = 0, 1, ..., γ − 1 + Figure 1. A conception of scale-free network m0). The probability that a vertex attaches to another vertex i is proportional to the rate an edge ki which a vertex i has[3][1]. Πi = Π(ki) ≡ ki j=τ−1+m0 j=0 kj (0 ≤ i<τ + m0) (1) The probability P(k) that a vertex in the network interacts with k other vertices decays as a power law, following P(k) ∼ k−γ. The collaboration graph of movie actors represents a well-documented example of a social network. Each actor is represented by a vertex, two actors being connected if they were cast together in the same movie. The probability that an actor has k links (characterizing his or her popularity) has a power-law tail for large k, following P(k) ∼ kγactor , where γactor = 2.3 ± 0.1. A more complex network with over 800 million vertices is the WWW, where a vertex is a document and the edges are the links pointing from one document to another. The topology of this graph determines the Web’s connectivity and, consequently, our effectiveness in locating information on the WWW. Information about P(k) can be obtained using robots, indicating that the probability that k documents point to a certain Web page follows a power law, with γwww = 2.1 ± 0.1[3]. Real networks have a competitive aspect, as each node has an intrinsic ability to compete for edges at the expense of other vertices[10]. They propose a model in which each node is assigned a fitness parameter ηi which does not change in time. Thus at every time step a new node j with a fitness ηj is added to the system, where ηj is chosen from a Proceedings of the 2005 International Workshop on Data Engineering Issues in E-Commerce (DEEC’05) 0-7695-2401-X/05 $20.00 © 2005 IEEE