s research work has emphasis on the 6) User preference crawler and user prefer rence recommender system This crawler is responsible for crawling user preference data, meas. 9) Search Function: Cosine similarity is a similarity which detail the research papers posted by each individual urement between two vectors of n dimensions. The user including a set of personally defined tags concept is finding the cosine of the angle between two vectors 7A Profiler: A profiler is a mechanism that exploits the This measurement is often used to compare documents in text use of preference data in the recommender mechanism to mining. Given two vectors of attributes, A and B, the cosine suggest a research paper that matches with user preferences similarity, 0, is calculated by the attributes dot product divided 8)User profile: A collection of personal data related to a by the magnitude as Equation (1) specific user. A profile refers to the explicit digital similarity= cos()=.4.B representation of a person's identity. User profiles can be JAB (1) considered as the computer representation of a user model Research Paper sharing System Po Search pape World Wide Web Post a paper Server Searc Seed l Research Paper Recommender System User preference Get data crawler Crawler Indexer research paper aper User preference data Keyword Search Function QI Index Profiler Search Ranking Function User profile d Research Paper Recommender System For text matching, the attribute vectors A and B are usually delivering personalized information. The prototype of the quency vectors of the documents. The cosine system and preliminary results are presented similarity can be seen as a method of normalizing document length during comparison. systems use collaborative filtering methods. However, our 5) Ranking: The number of people bookmarking each proposed method takes advantage of a set of user defined tags paper, the number of appearance of each paper in multiple of the posted paper. The recommender function system group users, a user defined priority of the paper, and consists of these two stages date/time/year of posted paperHowever, this research work has emphasis on the recommender system. 4) Search Function: Cosine similarity is a similarity measurement between two vectors of n dimensions. The concept is finding the cosine of the angle between two vectors. This measurement is often used to compare documents in text mining. Given two vectors of attributes, A and B, the cosine similarity, θ, is calculated by the attributes dot product divided by the magnitude as Equation (1). A B A B similarity . = cos(θ ) = For text matching, the attribute vectors A and B are usually term frequency vectors of the documents. The cosine similarity can be seen as a method of normalizing document length during comparison. 5) Ranking: The number of people bookmarking each paper, the number of appearance of each paper in multiple group users, a user defined priority of the paper, and date/time/year of posted paper can be used for ranking mechanism. 6) User preference crawler and user preference data: This crawler is responsible for crawling user preference data, which detail the research papers posted by each individual user including a set of personally defined tags. 7) A Profiler: A profiler is a mechanism that exploits the use of preference data in the recommender mechanism to suggest a research paper that matches with user preferences. 8) User profile: A collection of personal data related to a specific user. A profile refers to the explicit digital representation of a person's identity. User profiles can be considered as the computer representation of a user model, delivering personalized information. The prototype of the system and preliminary results are presented. 9) Recommendation mechanism: Most recommender systems use collaborative filtering methods. However, our proposed method takes advantage of a set of user defined tags of the posted paper. The recommender function system consists of these two stages: Figure 1. A Framework for Tag-Based Research Paper Recommender System Research Paper sharing System Post a paper Server Search paper World Wide Web Post a paper Search paper Group1 Group2 Community Research Paper Recommender System Get data User Query Search Seed URL Crawler research paper Indexer Search Function Ranking Keyword Result Paper Corpus Index Recommend Function User preference crawler Profiler User preference data User profile (1) 105