現今網際網路發達，大多數的學術期刊以數位資料庫來儲存，因此e化電子期刊是目前學術研究不可或缺的參考資源，其中又以Institute for Scientific Information (ISI)機構所建置JCR(Journal Citation Reports)資料庫中所列約8,000多種期刊，最常被國內學者參考使用，然而龐大的數位期刊卻容易造成使用者更大的負擔與迷失(Disorientation)。目前學界的教師大多以選擇分類及鍵入關鍵字並依年份來搜尋期刊論文，但查詢出的結果仍是資料量冗雜。此外，教師也常以目標發表的期刊來查詢，而沒考慮研究方向會隨時間而改變，導致教師不易找尋適合目前研究的期刊。
為了解決上述的問題，本研究提出一考量時間權重的期刊推薦方法，降低教師在選擇參考期刊時的茫然和負擔。首先，利用N-gram選詞法和字詞頻率(Term Frequency)的技術進行特徵字詞選取，再利用維基百科(Wikipedia)所開發的Java Wikimedia API來進行專有名詞查詢以選取出研究主題。因為教師和期刊的研究主題會隨時間而改變，一般來說，越近期發表的論文越重要，其研究主題越新穎。因此，本研究設定一時間權重(Time Weighting)，越早期發表的權重越低，相反的，越近期發表越高，來擷取更適合目前教師和期刊的研究主題。接著利用教師研究主題資料庫來建立一個參考向量(Reference Vector)，並以此參考向量來建構每位教師和各種期刊的研究主題之二元向量(Binary Vector)，接著利用二元向量空間(Binary Vector Space)進行教師和期刊的研究主題相似度比對，降低期刊推薦的複雜度。
本研究所提出的方法能找出更適合教師目前研究的期刊來加以推薦，避免教師按自己習慣性發表的期刊種類，或僅以影響指數(Impact Factor)來判斷，而忽略其它適合參考及發表論文的期刊，以符合教師在找尋期刊時的真正需求。

With the explosive growth of the emerging popularity of the Internet, it has the trend to publish researching papers on-line. The Institute for Scientific Information (ISI, now Thomas Reuters) has the most widely used online database (Journal Citation Reports, JCR) to provide the most valuable journals. The JCR Science edition contains data over 8,000 journals in science and technology. However, the mass of content available on the Internet arises a problem of information overload and disorientation. Currently, the teachers usually seek the researching papers by keying the keywords from academic search engine. However, the number of selected papers from the searching results is still very large. On the other hand, many teachers get used to using the target-based searching to seek the journal papers on-line. Since they don’t consider the factor of time-varying, it becomes difficult to find the appropriate journal papers to match their current studying.
To overcome above-mentioned problems, we propose a journal recommendation method based on considering time-weighting parameter. Firstly, we utilize the N-gram and term frequency (TF) to classify and categorize words. Secondly, we identify the keywords according to the Java Wikimedia API. Generally, we judge the newer papers to be more important than older papers. Therefore, in order to extract the suitable for researching topics, a time-weighting is set in our method according to time factor of the journal papers. Finally, we make a reference vector (RV) from the set of studying topics of teachers, and utilize the RV to set up the binary vectors of researching topics of teachers and journals. To reduce the complexity of the proposed method, we perform the similarity matching module in binary vector space.
In this thesis, we propose a time-aware journal recommendation method to seek appropriate journals to teachers. Experimental results show that the proposed approach can efficiently improve the accuracy of the recommendation.

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■ 網站資料
NIST, http://trec.nist.gov/data/reuters/reuters.html