Thom-Santelli [26] explored tags for communication in directions for presenting characterizations of CiteULike and social tagging, Budura [15] presented HAMLET to promote Bibsonomy that target the management of scientific literature an efficient and precise reuse of shared metadata in highly Noel [24] looked at the tagging behavior of people who were dynamic content where tags are scarce; Li [23] used the self describing four frequently entered references. The techniques rganizing characteristics of SOM neural networks to classify from CiteULike have been applied to other academic search popular tags within the "delicious"website; Choochaiwattana such as Farooq et al. [191,[21] presenting four novel 17 examined the use of social annotations to improve the implications for designing Cite Seer quality of web searches; Gelernter [20 compared th Toine Bogers [27] employed CiteULike to generate reading information retrieval value of the cloud format tags and the tag lists for scientific articles based on the user's online reference words themselves as found in the Library Thing catalog Schmitz [10] utilized association rules mining to analyze and library. They applied three different CF algorithms and found structure folksonomies. The results can be used for ontology that user-based filtering performs the best Ms. g and supporting emergent semantics IIL. A FRAMEWORK FOR TAG-BASED RESEARCH PAPER Many researchers tried to improve the recommender RECOMMENDER SYSTEM system: Vig [38] introduced"tagsplanations", which are the explanations-based community tags. They also examined which types of tags are the most useful for tagsplanations illustrated in Figure 1. The framework is divided into a two Forsati [39] presented the HITs algorithm for part system: research paper sharing and a research paper recommender recommendation sets and showed how this method can improve the overall quality of web recommendations and other A. Research paper sharing system researchers[12],[13],[14,[22l,[29].[3land33J. A research paper sharing system provides users with new Researchers who tried to improve search results on the ways to share their research interests. They can post and research paper websites using indexes that were also applied comment on papers. They can also discover interesting papers integrating the results into e-learning resources such as [3 or Sed by other users who share the same interests. This kind to abstracts include [5], [7,[8], and 19]. There are works or ers to create their own keywords for and into research paper from libraries such as [2] [4] attaching to the posted papers. These keywords are known as s. Tags provide user-defined terms for a paper. The interesting part is that these tags can be used to create a profile B. Academic social bookmarking for each user. A research paper recommender mechanism Web searches based on social bookmarking, which let users could take advantage of this created user profile in research pecify their keywords of interest, or tags on web resources have become increasingly popular. One of the most famous (www.Citeulike.Org),aweb-basedsocialbookmarking B. A Research Paper Recommender system 1) Crawler: A research paper crawler is a small been available as a free web service since november 2004 computer program that browses directly to the research paper Like many successful software tools, CiteULike has a flexible sharing systems of the www in a predetermined manner. The filing system based on tags. Tags provide an open, quick and research paper crawler is responsible for gathering research user-defined classification model that can produce interesting paper information such as paper author, tags used, etc. This useful information helps the system to determine a user's Benefits from these innovative and time-saving scholarly interests and also helps the system to create index for each bookmarking services includes: one-click extraction of paper. Java programming is used to implement a crawler on others are reading and for sharing resources with peers, export 2) Research Paper corpus: Paper corpus is a collection of libraries in various citation formats, import existing libraries, research papers extracted from the research paper sharing access from any computer with Internet connections. It can be easily seen that CiteULike is more than just your personal 3)Indexer: TF-IDF (term frequency-inverse document research library. Additionally, it is also capable to frequency) will be used for creating indices. TF-IDF is a weight often used in information retrieval and text mining tag papers into categories This weight is a statistical measure used to evaluate how add your own comments important a word is to a document in a collection or corpus allow others to see your library The importance increases proportionally to the number of o The components of CiteULike are registered and add times a word appears in the document but is offset by the bookmarks frequency of the word in the corpus. [36] and [37] showed that Research works related to academic search engines include: research paper information like"tag, title and abstract " could Capocci [16] analyzed the small-world properties of the be a useful source for creating indices for research papers CiteULike folksonomy. Santos-Neto [28] explored three mainThom-Santelli [26] explored tags for communication in social tagging; Budura [15] presented HAMLET to promote an efficient and precise reuse of shared metadata in highly dynamic content where tags are scarce; Li [23] used the self organizing characteristics of SOM neural networks to classify popular tags within the “delicious” website; Choochaiwattana [17] examined the use of social annotations to improve the quality of web searches; Gelernter [20] compared the information retrieval value of the cloud format tags and the tag words themselves as found in the Library Thing catalog. Schmitz [10] utilized association rules mining to analyze and structure folksonomies. The results can be used for ontology learning and supporting emergent semantics. Many researchers tried to improve the recommender system: Vig [38] introduced ‘‘tagsplanations’’, which are the explanations-based community tags. They also examined which types of tags are the most useful for tagsplanations. Forsati [39] presented the HITS algorithm for recommendation sets and showed how this method can improve the overall quality of web recommendations and other researchers [12], [13], [14], [22], [29], [31], and [33]. Researchers who tried to improve search results on the research paper websites using indexes that were also applied to abstracts include [5], [7], [8], and [9]. There are works on integrating the results into e-learning resources such as [3], and into research paper from libraries such as [2], [4]. B. Academic social bookmarking Web searches based on social bookmarking, which let users specify their keywords of interest, or tags on web resources, have become increasingly popular. One of the most famous social bookmarking websites in academia is CiteULike (www.CiteULike.org), a web-based social bookmarking service and traditional bibliographic management tool. It has been available as a free web service since November 2004. Like many successful software tools, CiteULike has a flexible filing system based on tags. Tags provide an open, quick and user-defined classification model that can produce interesting new categorizations. Benefits from these innovative and time-saving scholarly bookmarking services includes : one-click extraction of bibliographic references, tag and rate user references on what others are reading and for sharing resources with peers, export libraries in various citation formats, import existing libraries, access from any computer with Internet connections. It can be easily seen that CiteULike is more than just your personal research library. Additionally, it is also capable to: • ‘tag’ papers into categories • add your own comments on papers • allow others to see your library The components of CiteULike are registered and add bookmarks. Research works related to academic search engines include: Capocci [16] analyzed the small-world properties of the CiteULike folksonomy. Santos-Neto [28] explored three main directions for presenting characterizations of CiteULike and Bibsonomy that target the management of scientific literature. Noël [24] looked at the tagging behavior of people who were describing four frequently entered references. The techniques from CiteULike have been applied to other academic search such as Farooq et al. [18], [19], [21] presenting four novel implications for designing CiteSeer. Toine Bogers [27] employed CiteULike to generate reading lists for scientific articles based on the user’s online reference library. They applied three different CF algorithms and found that user-based filtering performs the best. III. A FRAMEWORK FOR TAG-BASED RESEARCH PAPER RECOMMENDER SYSTEM A framework for tag-based research paper search engine is illustrated in Figure 1. The framework is divided into a two part system: research paper sharing and a research paper recommender. A. Research paper sharing system A research paper sharing system provides users with new ways to share their research interests. They can post and comment on papers. They can also discover interesting papers posed by other users who share the same interests. This kind of system allows users to create their own keywords for attaching to the posted papers. These keywords are known as tags. Tags provide user-defined terms for a paper. The interesting part is that these tags can be used to create a profile for each user. A research paper recommender mechanism could take advantage of this created user profile in research paper recommendations. B. A Research Paper Recommender system 1) Crawler: A research paper crawler is a small computer program that browses directly to the research paper sharing systems of the WWW in a predetermined manner. The research paper crawler is responsible for gathering research paper information such as paper author, tags used, etc. This useful information helps the system to determine a user's interests and also helps the system to create index for each paper. Java programming is used to implement a crawler on this framework. 2) Research Paper corpus: Paper corpus is a collection of research papers extracted from the research paper sharing system. 3) Indexer: TF-IDF (term frequency–inverse document frequency) will be used for creating indices. TF-IDF is a weight often used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. The importance increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus. [36] and [37] showed that research paper information like “tag , title and abstract” could be a useful source for creating indices for research papers. 104