66 com Figure 3. An example of constructed network vance. The relevance means the similarity value. As shown co-occurrence, we can see the change of relation among in Figure 4, Papits can recommend several papers in de- words, and solve the characteristic(2)mentioned in Section sending order of similarity, based on our recommendation 1,1.e, whether or not a paper is novel mechanism We measure the frequencies of word co-occurrence and the recencies of word co-occurrence to divide into four sit 2.3. Construction of topic model uations as follows: This section presents how to construct the topic model. h elated to the keywords is commonly Our method uses a huge papers from a database of Papits known to construct the topic model which based on frequencies of word co-occurrence and recencies of word co-occurrence If the frequency of word co-occurrence is low, the re- A database of Papits contains bibliographical information earch topic related to te keywords is not known. Few of information technology articles, which includes the year researchers show interest in the topictopic of publication, and Papits can retrieval the information ac If the recency of word co-occurrence moves upward cording to the year of publication. By observing the fre- the research topic related to the keywords is hot and quencies of word co-occurrence and recencies of word roIs Proceedings of the 2005 International Workshop on Data Engineering Issues in E-Commerce(DEEC'05) 076952401-X0520.00@2005LEEE SOCIETYFigure 3. An example of constructed network vance. The relevance means the similarity value. As shown in Figure 4, Papits can recommend several papers in de￾scending order of similarity, based on our recommendation mechanism. 2.3. Construction of topic model This section presents how to construct the topic model. Our method uses a huge papers from a database of Papits to construct the topic model which based on frequencies of word co-occurrence and recencies of word co-occurrence. A database of Papits contains bibliographical information of information technology articles, which includes the year of publication, and Papits can retrieval the information ac￾cording to the year of publication. By observing the fre￾quencies of word co-occurrence and recencies of word co-occurrence, we can see the change of relation among words, and solve the characteristic(2) mentioned in Section 1, i.e.,whether or not a paper is novel. We measure the frequencies of word co-occurrence and the recencies of word co-occurrence to divide into four sit￾uations as follows: • If the frequency of word co-occurrence is high, the research topic related to the keywords is commonly known. • If the frequency of word co-occurrence is low, the re￾search topic related to te keywords is not known. Few researchers show interest in the topic. topic. • If the recency of word co-occurrence moves upward, the research topic related to the keywords is hot and promising. Proceedings of the 2005 International Workshop on Data Engineering Issues in E-Commerce (DEEC’05) 0-7695-2401-X/05 $20.00 © 2005 IEEE