A Paper Recommendation Mechanism for the Research Support System Papits Satoshi Watanabe, Takayuki Ito, Tadachika Ozono and toramatsu shintani Graduate School of Engineering, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan watanabe, itota, ozono, tora @ics. nitech ac jp Abstract called Papits[8J118(13. Papits has several functions that allow it to manage research information by paper sharing, ve have developed Papits, a research support system, paper recommending, paper retrieving, paper classifying that shares research information, such as PDF files of re search papers, in computers on networks and classifies the the sharing of research information, such as the PDF files of nformation into research types. Papits users can share var- research papers, and to collect papers from Web sites ious research information and survey the corpora of their The recommendation function constructs a users model particular fields. To develop Papits, we need to design to determine a user's research interests and specialties. This mechanism to identify a user's interest. Also, when cor model is constructed by analyzing structing an effective paper recommendation system, it is user has viewed and enable them to recommend papers important to carefully create user's models. We propose a ased on their interest. The recommendation in Papits grad- ually improves accuracy through paper viewing history of work. The scale-free network has vertices and edges, and users. This particular paper focuses on the paper recom ensures growth by ' preference attachments. Our method mendation. One of the main problems associated with the applies a paper viewing history to construct a scale-free recommendation is how to reduce information overload and network based on the word co-occurrence. a constructed realize a precise and accurate recommendation. In conven network consists of vertices that represent words, and edge tional research of natural language processes, the TF-IDF that represent the word co-occurrence In our method, a pa method[21]et al., was used to give added weight to words per is added to the network as indicated by a user's paper for searching or summation. Also, the TF-IDF method is viewing history. Additionally we define the 'topic weight often used to calculated the importance of and a similari- By using two elements; the topic frequency and the topic re- ties between documents. The calculation, however, does not cency, we calculate the topic weight. By using the word co- take the differences between the users'interest into account occurrence in a database, we measure the topic frequency. As each user's interest is different, so a weight of word is Also, by using the Jaccard coefficient, we measure the topic different for every user. We proposed and applied a recom- recency. Our result indicates that our method can effec mendation mechanism to Papits that uses the user's paper tively recommend documents for Papits users viewing history to reflect a user's interests or specialties By using a recommendation mechanism, we can discover several papers in various databases, but each paper can be classified according to the following characteristics 1. Introduction (I)Does the paper have an important fact or not? (2)Does the paper have a novel fact or a known fact? ()Does the paper have a fact that is interesting to the user As information technology becomes an indispensable part of our daily life, huge amount of information is shared When constructing a user's model, ideally we would like throughout the world. The speed and amount of this shar to discover papers that are important, novel, and of inter ing has accelerated with the advent of the Internet and users est to the user. Conventional recommendation mechanisms are becoming overloaded with information. with such valid mainly deal with the characteristics(1); the importance of and noisy information, we need tools to identify useful in- formation or knowledge that meets demands of individual mechanisms rank papers by using the precision and recall user. So, we have developed a research support system 'PAPer Information Tailor System Proceedings of the 2005 International Workshop on Data Engineering Issues in E-Commerce(DEEC'05) 076952401-X0520.00@2005LEEE SOCIETYA Paper Recommendation Mechanism for the Research Support System Papits Satoshi Watanabe, Takayuki Ito, Tadachika Ozono and Toramatsu Shintani Graduate School of Engineering, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan {watanabe, itota, ozono, tora}@ics.nitech.ac.jp Abstract We have developed Papits, a research support system, that shares research information, such as PDF files of re￾search papers, in computers on networks and classifies the information into research types. Papits users can share var￾ious research information and survey the corpora of their particular fields. To develop Papits, we need to design a mechanism to identify a user’s interest. Also, when con￾structing an effective paper recommendation system, it is important to carefully create user’s models. We propose a method to construct user’s models using the scale-free net￾work. The scale-free network has vertices and edges, and ensures growth by ‘preference attachments’. Our method applies a paper viewing history to construct a scale-free network based on the word co-occurrence. A constructed network consists of vertices that represent words, and edges that represent the word co-occurrence. In our method, a pa￾per is added to the network as indicated by a user’s paper viewing history. Additionally we define the ‘topic weight’. By using two elements; the topic frequency and the topic re￾cency, we calculate the topic weight. 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. Our result indicates that our method can effec￾tively recommend documents for Papits users. 1. Introduction As information technology becomes an indispensable part of our daily life, huge amount of information is shared throughout the world. The speed and amount of this shar￾ing has accelerated with the advent of the Internet and users are becoming overloaded with information. With such valid and noisy information, we need tools to identify useful in￾formation or knowledge that meets demands of individual user. So, we have developed a research support system, called Papits1[8][18][13]. Papits has several functions that allow it to manage research information by paper sharing, paper recommending, paper retrieving, paper classifying and a research diary. The paper sharing function facilitates the sharing of research information, such as the PDF files of research papers, and to collect papers from Web sites. The recommendation function constructs a user’s model to determine a user’s research interests and specialties. This model is constructed by analyzing research papers that a user has viewed and enable them to recommend papers based on their interest. The recommendation in Papits grad￾ually improves accuracy through paper viewing history of users. This particular paper focuses on the paper recom￾mendation. One of the main problems associated with the recommendation is how to reduce information overload and realize a precise and accurate recommendation. In conven￾tional research of natural language processes, the TF-IDF method[21] et al., was used to give added weight to words for searching or summation. Also, the TF-IDF method is often used to calculated the importance of and a similari￾ties between documents. The calculation, however, does not take the differences between the users’ interest into account. As each user’s interest is different, so a weight of word is different for every user. We proposed and applied a recom￾mendation mechanism to Papits that uses the user’s paper viewing history to reflect a user’s interests or specialties. By using a recommendation mechanism, we can discover several papers in various databases, but each paper can be classified according to the following characteristics. (1)Does the paper have an important fact or not? (2)Does the paper have a novel fact or a known fact? (3)Does the paper have a fact that is interesting to the user at the present moment? When constructing a user’s model, ideally we would like to discover papers that are important, novel, and of inter￾est to the user. Conventional recommendation mechanisms mainly deal with the characteristics(1); the importance of papers, for example, by using a statistics approach. Some mechanisms rank papers by using the precision and recall 1PAPer Information Tailor System Proceedings of the 2005 International Workshop on Data Engineering Issues in E-Commerce (DEEC’05) 0-7695-2401-X/05 $20.00 © 2005 IEEE