A Decision Support System for Finding Research Topic based on Paper Recommendation

Association for Information Systems AIS Electronic Library (AISeL) PACIS 2013 Proceedings Pacific Asia Conference on Information Systems (PACIS) 6-18-2013 A Decision Support System for Finding Research Topic based on Paper Recommendation Li Yu Renmin University of China, buaayuli@ruc.edu.cn Jie Yang Renmin University of China, moon-knigth@qq.com Dong Yang Renmin University of China, yangdong519@126.com Xiaoping Yang Renmin University of China, yang@ruc.edu.cn Follow this and additional works at: http://aisel.aisnet.org/pacis2013 Recommended Citation Yu, Li; Yang, Jie; Yang, Dong; and Yang, Xiaoping, "A Decision Support System for Finding Research Topic based on Paper Recommendation" (2013). PACIS 2013 Proceedings. 190. http://aisel.aisnet.org/pacis2013/190 This material is brought to you by the Pacific Asia Conference on Information Systems (PACIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in PACIS 2013 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org.

A DECISION SUPPORT SYSTEM FOR FINDING RESEARCH TOPIC BASED ON PAPER RECOMMENDATION Li Yu, School of Information, Renmin University of China, Beijing, P.R.China, State Key Laboratory of Digital Publishing Technology, Beijing, P.R.China, buaayuli@ruc.edu.cn Jie Yang, School of Information, Renmin University of China, Beijing, P.R.China, moon-knigth@qq.com Dong Yang, School of Information, Renmin University of China, Beijing, P.R.China, yangdong519@126.com Xiaoping Yang, School of Information, Renmin University of China, Beijing, P.R.China, yang@ruc.edu.cn Abstract Finding a good research topic is very crucial for a researcher, especially a new researcher, such as PhD student. Generally, a PhD student s research interest in the beginning is not clear, and he have to spend much time reading a lot of research literatures, step by step, his research preference will become clearer and clearer, and easier to find an advanced research topic in which he is really interested. So, reading a certain number of research literatures is critical for a PhD student to find final research topic. But, which literature should be selected to read? How to quickly clear user preference and determine final research topic by reading fewest literatures? As far as we known, there is no system used to support the user to find research topic. In the paper, a research topic decision support system is originally proposed and creatively developed to help a user to quickly find potential research topic based on iteratively paper recommendation. Starting from user s initial unclear research preference, through continuous recommending appropriate literatures to the user, then the user read and critiques them by rating until he find final research topic. During the process, since the user read the most appropriate literature in each round, he can quickly find the research topic while spending minimal time. Keywords: Decision Support System, Recommender System, Human Computer Interaction, Research

1. INTRODUCTION Finding a good research topic is very crucial to a researcher, especially a new researcher, such as master student and PhD student. According to the statistic, there new 580.000 master students are admitted each year in China. That is to say, there so many students need to find their interesting research topic for their degree thesis every year. But how they find their interesting research topic? In general, by focusing on their tutor s research field, the students select special research topic according to their interests after reading a lot of related literatures. Unfortunately, many graduated students are force to re-select another research topic owing to various reasons. For example, after making research for a several months, it is found that the selected research topic has been studied by other researcher, or the selected research topic is not enough original and novel. The most important reason is that they do not read enough appropriate literature during the process of selecting research topic. It is true that re-selecting a new research topic will delay their graduation. In this paper, a research topic decision support system is originally proposed and creatively developed to help a user to quickly find his research topic based on iteratively paper recommendation. The proposed system recommends most appropriate literatures to the graduated students, and then the students provide their feedback after they read recommended literatures. Based on their feedback, the system recommends most appropriate literature to the students once again. After many times repeatedly like this, the system will recommend the students appropriate and comprehensive related literatures suited to students interests according to the feedbacks. And the system will helping our students rapidly find their interests and research field that he is really interested in, eventually to help students rapidly determine their research topic. The rest of the paper is organized as follows. Some related techniques and works are summarized in next section. In section 3, the system framework is proposed. A lot of key issues in the system are detail in section 4, such as how to acquire and update user preference and how to recommend literature. In section 5, prototype system is developed in section 5. Finally, conclusions are given and further researches are discussed. 2. RELATED WORKS Firstly, information retrieve is the most widely used in literature retrieve and recommendation, it returns corresponding results according to keyword inputted by user. Also, citation analysis is also widely used in literature retrieve field (Gipp & Beel 2009; Matsatsinis etc al. 2007), which is based on the hypothesis that if a paper is quoted by another paper, then two papers are similar about content to a certain extent. Other literatures recommendation methods include journal impact factor, author association, keywords analysis (Vellino2009; Watanabe etc al.2005). Although a lot retrieve techniques are widely used in almost every literature database and digital library, but more strictly speaking, they are not personalized recommendation techniques in the strict sense. The strictly recommendation was first studied by Resnick and Varian. Since then, a lot of researches are made in this field (Vellino 2009; Rolando 2006; Dmitry 2013). Current researches on recommendation share following common characteristics. First, there exists an implied hypothesis in current research that user customer s preference must be clear in order to succeed recommend. Sometimes users preferences are explicit while their preferences are implicit. If there have users implicit preference, data mining method should be used to get their preference. Secondly, the objective of traditional literature recommender system is only to give a recommended literature list, not to help users to complete a task. Thirdly, core pare of current literature recommendation system is recommendation algorithm. Numerous research works are dedicated to this field. Collaborative filtering recommendation (Pu 2006) is most widely studied, especially several famous problem, such as sparsity, cold-starting etc. In addition, other recommendation methods include content-based recommendation, knowledge based recommendation, association rule based recommendation, and

hybrid techniques mixed other algorithms. Seeing from the view of technology, the purpose of information retrieve is the maximize similarity degree between retrieved literatures and inputted keywords by users. In practice, a new researcher, such as PhD candidate students, it is impossible to clearly know special keywords corresponding to their future research topic. In summary, although information retrieve technology and recommendation technology are the core technologies in the process of selecting research topic, they are hard to directly be used to find research topic for PhD candidate students. There are two reasons. Firstly, users preferences are unclear or at least non-specific at the beginning phase. Secondly, recommended literatures for finding research are not only associate with users preferences, but also associated with the some characteristics of literatures, such as publication date, authoritative, journal grade and so on. In recent years, conversational recommender system emerged to clear users preferences based on conversation between users and system in order to improve accuracy of recommendation (Rachael R. & Barry 2005; Ricci & Nguyen 2008; Rolando 2012). For example, Ricci made research on shopping recommendation in the environment of mobile business by the method of critique (Ricci & Mahmood 2007; Ni etc al. 2012) while Mark studies shopping recommendation based on customer scenarios information which were produced by meeting alternately feedback, such as location, time, weather and so on. Overall, there is very few about selecting research topic. 3. SYSTEM FRAMEWORKS 3.1 Research Scenario In this paper, our work is to develop a decision support system for finding research topic based on literature recommendation. Our research scenario can be illustrated through the following study case. Kai WANG is a new PhD candidate student admitted by department of computer science in ABC University. His advisor is professor Li, well known in data mining field. On the first day when Kai WANG begins his PhD study, Professor Li talks with Kai WANG about selecting research topic for PhD thesis. Professor Li said he will not give Kai WANG a special topic as the title of his future PhD thesis because professor Li hope to give Kai WANG more freedom to find an interesting research topic in which Kai WANG is also really interested. But the research topic is related to data mining because professor Li is an expert in data mining, and he will provide Kai WANG with more guidance during his research. Also, professor Li require Kai WANG read a lot of literatures before he determine final research topic. So Kai WANG searches paper by the keyword data mining in the literature database. Unfortunately, one and half of year later, although WANG read 80 papers retrieved in literature database, he still did not find his research topic because there are so many research directions and more than one million literatures related to data mining in literature database. In fact, although WANG read more than 80 literatures, many literatures are similar, and some literatures have no much reference value for other reasons, such as too old, not authority. If doing as the current speed, WANG think he can read 300 literatures at most, and he still cannot determine research topic after four years. For this reason, WANG is very anxious. So, he very hopes there is a system to support him in finding research topic. By the help of system, WANG will quickly find research topic through reading the fewest literatures. Our research in the paper is devoted to developing this kind of system for helping WANG to quickly find research topic through reading the fewest literatures. 3.2 System Frameworks In order to help WANG to quickly find research topic through reading the fewest literatures, the most key idea is to recommend the most appropriate literature to WANG at appropriate time. The system framework of our proposed system is described in Figure 1. In the beginning, WANG put into his

initial blurred preferences with the keyword data mining, and then the system will recommend WANG five representative papers retrieved in literature database based on his initial blurred preferences. After reading the five classical papers, WANG will know the directions related to data mining field to some degree, and he will has more clear preference. Then, WANG is required to clique this five papers by rating according to the following visual angle, such as whether the paper is enough new, or whether he is interested, or whether the paper is beneficial to finding research topic. At the same time, WANG is also allowed to input new keywords in which he is interested. Based on new inputted keywords and his critiquing for recommended papers, the system will recommend another five most appropriate papers once again. In general, newly recommended papers are more special and are more interesting to WANG. In fact, these newly recommended papers are also more focused. Step by Step, after many iterations, WANG will has more clear research preference till he think the research topic is found. In fact, if the recommended literature is enough focused, it is reasonable to think that WANG find research topic. Under this condition, the system will stop. Initial Keywords Literature Database Updating User Preference based on feedbacks Generate recommended literatures Clique recommended literatures No Whether topic is found? End Yes Figure 1. System Framework User preference is acquired by following learning processes, as shown is Figure 2. 4. KEY TECHNIQUE ISSUES In order to implement the system, there are some key technique issues, including getting and updating user preference, recommending literature based updated research preference, and regarding when to end work. 4.1 How to get user s research preference

Because user s research preference is not clear in the beginning, it is very key to get and clear user s preference. In proposed system, there are two kinds of methods to get user s preference, respectively explicit keywords and implicit critique. Initial Preference Recommend Recommended Literatures Critique User Updated Preference Updating Preference Feedback information Figure 2. User Preference Learning 4.1.1 Explicit Keywords At any time, the user is allowed to input his interesting keywords to express his research preference. At the beginning, the user generally has very vague preference, explicitly inputted keywords is important for user. For example, in above research scenario case in section 3.1, WANG has a rough preference in data mining field. So, WANG can only input data mining as keyword. In the later, additional keywords can be inputted again till the user finds his research interesting topic. 4.1.2 Implicit Critique In general, a PhD candidate is hard to have explicit and special keyword preferences because he does not know well about a certain field. That is to say, he does not know which keywords are his explicit preferences. But he can critique them by rating according to his implicit research preference if some papers are recommended to him. In proposed system, the user is allowed to implicitly critique by rating recommended papers, respectively with Very Interested (1.0), Interested (0.8), Normal (0.5), Little Interested (0.2) and No Interested (0). 4.2 Updating User Preference Another important issue is how to learn and update user preference based on explicit keywords and implicit critiquing at each iteration. In the proposed system, user research preferences are updated though following three steps. Step 1:Summarize preference on critiqued papers as critiqued preference. For recommended paper set P at each round, the user will be required to critique them by rating. Based on critiquing, each keyword in each critiqued paper will be assigned a weight to measure its importance in user s preference. For each keyword appeared in anyone of all critiqued papers, its whole weight will be computed by weightily summing its weight in all critiqued papers. All keyword and their whole weight at certain round forms user critiqued preference at this round. Step 2:Integrate critiqued preference with explicitly inputted keywords as added preference. Including critiquing paper, a user is also allowed to input new keywords to explicitly express his

preference when he has more clear preference on his future research topic. It is called as keyword preference in this paper. And both keyword preference and critiqued preference are assigned different weight to show their importance for generating added preference. Specially, critiqued preference is assigned with weight α while keyword preference is assigned with weight (1-α). Formally, Added_Prefer(u)= α*critiqued_prefer(u)+ (1-α)*Keyword_Prefer(u) (1) Step 3:Combine old preference with added preference to generate candidate updated preference. Once added preference is generated, it will be used to update user preference by combining with user old preference. In proposed method, added preference is assigned with weight β while old preference is assigned with weight (1-β). More the weight β is, more important the added preference is for generating candidate user preference. Formally, Cand_Updated_Prefer(u)= β*added_prefer(u)+ (1-β)*Old_Prefer(u) (2) Step 4:Shorten candidate updated preference to final updated preference. For candidate updated preference, there are a lot of preference keywords with very little value. These preference keywords have not much role in describing user preference, in order to simplify compute, all keywords are descendingly sorted by their value. Once accumulated value is over 0.9, surplus keywords are neglected. Final updated preference is generated by accumulated keywords. Then, their value is standardized. In the following, an example is given to illustrate above proposed method. In this example, it is supposed that user old preference is shown in Table 1. Preference Keywords Value Personalized Recommendation 0.4 Recommender System 0.2 Data Mining 0.3 Collaborative Filtering 0.1 Table 1. Old preference Based on above user preference, there some papers are recommended to the user. After the user read these papers, he gives his feedback by critiquing them though rating as shown in the following Table 2. Paper 1 Paper 2 Paper 3 Keywords of Critiqued Papers Recommender System, Collaborative Filtering, Recommendation based on Content Data Mining, Associate Rule, Personalized Recommendation Collaborative Filtering, Knowledge Filtering, Recommender System Rating Very Interested (1) Normal (0.5) Little Interested (0.2) Table 2. Critique by rating recommended paper Including his critiquing on recommended paper, the user also input interested keywords as shown in the following Table 3. E-Commerce, Recommender System, Collaborative Filtering

Table 3. Explicit inputted keywords In order to update user preference, added preference is generated by integrating critiqued preference and newly inputted keywords. In this paper, weight α is set as 0.6 and weight β is set as 0.3. According to the equation (1), added preference is shown in following, Preference Keywords Value Recommender System 0.274 Collaborative Filtering 0.274 E-Commerce 0.133 Recommendation based on Content 0.118 Data Mining 0.059 Associate Rule 0.059 Personalized Recommendation 0.059 Knowledge Filtering 0.024 Table 4. Added preference Similarly, according to equation (2), candidate updated preference is computed by combining added preference with old preference, as shown in the following Table 5. Preference Keywords Value Recommender System 0.165 Collaborative Filtering 0.179 E-Commerce 0.054 Recommendation based on Content 0.048 Data Mining 0.227 Associate Rule 0.024 Personalized Recommendation 0.294 Knowledge Filtering 0.010 Table 5. Candidate updated preference Then order all preference keyword by their value. When accumulated value is over 0.9, surplus keywords is neglected. Final updated preference is generated by accumulated keywords. Then, their value is standardized, as shown in Table 6. So, final updated preference at this iteration is shown in Table 7. Preference Keywords Order Value Accumulated Personalized Recommendation 0.294 0.294

Data Mining 0.227 0.521 Collaborative Filtering 0.179 0.700 Recommender System 0.165 0.865 E-Commerce 0.054 0.919 Recommendation based on Content 0.048 0.967 Associate Rule 0.024 0.990 Knowledge Filtering 0.010 1.00 Table 6. Order and Accumulative preference Preference Keywords Accumulated Personalized Recommendation 0.340 Data Mining 0.262 Collaborative Filtering 0.207 Recommender System 0.191 Table 7. Final updated preference 4.3 Recommend Literature Once user updated preference is generated at certain iteration, it is used to recommend literatures for user to read. For each paper related to a field in literature database, its recommendation value is computed. In our proposed method, listed keywords in each paper are considered to match user updated preference keywords. That is to say, if listed keywords list in a paper are more matched user updated preference keywords, then the paper has large recommendation value. Formally, all preference keywords set of user u is denoted as UP_KW while all listed keywords set in a paper is denoted as Pa_KW. For each keyword, its value in user preference is weight_user(a) while its value in a paper is denoted weight_pa(b) for each keyword. The weight weight_pa(b) is averaged by the number of listed keywords in a paper. That is to say, if there 4 keywords listed in a paper, then each keyword in the paper, the weight_pa(b) is equal to 0.25. Based on above notation, for each paper, its recommendation value is computed as following equation, In the following, an example is given. Here it is supposed that user updated preference is shown in Table 8. Also there are two papers, and their listed keywords and recommendation value are following as shown in Table 8. Paper Listed Keywords in the paper Recommendation Value Paper 1 Recommender System, Collaborative Filtering, E-Commerce, Web2.0 0.207*0.25+0.191*0.25=0.100

Paper 2 Collaborative Filtering, Data Mining, Associate Rule 0.207*0.333+0.262*0.333=0.156 Table 8. Two papers and their recommendation value 4.4 When to end work Once research topic is found, the system will end work. How to decide the research topic has been found? In our proposed system, two cases are set to decide research topic is found. The first one is that the system will automatically ends work once if the similarity between recommended literatures is over given threshold. In fact, if the similarity between recommended literatures is enough, it is considered that these recommended literatures are closely related to a certain topic, and it is reasonable to think that the user had found a topic. The topic which recommended literature detailed is the topic which the user wants to find and research. The second case is that the user may manually end work of system by pressing a button if the user thinks he had found research topic at any time. A user may select anyone case to end work of the system. 5. DEVELOPED PROTOTYPE SYSTEM In this section, a prototype system for finding research topic is developed. In current version, literature database used in prototype system is CNKI, which is a Chinese literature database. In order to begin to work, user is required to initially input interest field keyword, such as Database in Chinese, as shown in Figure 3. Figure 3. Initially input interest field keyword Once initial keyword is inputted, five classical literatures related to database field are recommended to the user to read, as shown in Figure 4. The similarity of all recommended papers is computed and user current preference is generated, as shown in Figure 5.

Figure 4. Recommended literatures Figure 5. Similarity of recommended literatures and current user preference For each recommended literature, the user can see its detailed information, such as author, keywords, published journal and abstract, as shown in Figure 6. Also, the paper can be directly downloaded. Figure 6. Detail information of recommended literature As shown in Figure 7, the user can critique recommended papers by rating them. Figure 7. Critique recommended literature by rating

When the similarity of recommended papers is large given threshold value 0.8 after many iterations, it is considered that the user have find the research topic. If the user thinks he cannot find research topic, he can press Go On to let the system continue work, as shown in Figure 8. Figure 8. Paper similarity is larger given threshold 6. CONCLUSION To find a good research topic is very crucial for a researcher as soon as possible, especially a new researcher, such as PhD student. Although information retrieve techniques, recommendation techniques etc. are important to get some useful literature recommendation if users preference is clear, they could not help users to seek a research topic when his research is not clear. In this paper, a novel system is proposed to help a user to quickly find research topic based on paper recommendation even the user s research preference is not clear. Starting from user s initial unclear research preference, through continuous recommending appropriate literature to the user, then the user read and critiques them by rating until the user find the final research topic. During the process, since the user read the most appropriate literature in each round, he can quickly find the research topic by spending minimal time. Although a research topic decision support system is originally proposed and creatively developed, there still have a lot of work to be done for further improving its performance and accuracy in the future. For example, it is better if the similarity between keywords is computed based on their semantics than simply matching of keywords. ACKNOWLEDGEMENTS This work was supported by National Natural Science Foundation of China(No.71271209), Beijing Natural Science Foundation(No.4132067), Humanity and Social Science Youth Foundation of Ministry of Education of China(No.11YJC630268), the Opening Project of State Key Laboratory of Digital Publishing Technology, the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China. References Dmitry, B. (2013). Semantic audio content-based music recommendation and visualization based on user preference examples. Information Processing and Management: an International Journal.Vol.49,Issue.1; January 2013. Garcia, I., Pajares, S., Sebastia, L. and Onaindia, E. (2012). Preference elicitation techniques for group recommender systems, Information Sciences, 189 (2012) 155 175 Gipp, B. and Beel, J. (2009). Identifying related Documents for Research Paper Recommender by CPA and COA, Proceedings of the World Congress on Engineering and Computer Science, Vol I, WCECS 2009, October 20-22, 2009, San Francisco, USA Gipp, B., Beel, J. and Hentschel, C. (2009). Scienstein: A Research Paper Recommender System. In Proceedings of the International Conference on Emerging Trends in Computing (ICETiC 09), pp.

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