合p x回M/w424k中N0年单WA人+机t15 sintanl Lab. Google NT Badminton@ Yahoo! JAPAN Researchindex@ Apple Lve Home Page *M-k(@ Apple Store @Tools Papers The recommending home search recommen register 力子刂横索 Relevance Bundle Design in Robust Combinatorial Auction Makoto Yokoo yuko Protocol against False-name Sakurai Shigeo Matsubara prvate ibrary 力元少22 NTATION Satisfiability Problems wit risen an REASONING Stochastic Local Sea Timothy J. Peugniez SEARCH Relational Learning via Propositional Algorithms An Dan Roth Wen-tau Yih NT Information Extraction Case. Study The Exponentiated Subgradient Algorithm for Dale Schuurmans and MODELING Heuristic Boolean Finnegan Southey Robert Programming C Holte PLANNING ,Exploiting Multiple Secondary O htemetzon Figure 4. An interface for paper recommendation If the recency of word co-occurrence moves down- By comparing Rwnwm(t)to Rwn wm(t-1), we calculate ward, the research topic related to the keywords is an the recency between words wn and wn i. e, the recency of closing. topic moves upward or downward In order to select from papers in a database of Papits, we calculate the topic weight Tun. tm as follows Tun Um Tfreq wn, wm) nvn(t-1)≠0) recent Recency(wn, Wm)= Run tom(t) where, Wn and w'm are words which co-occur in a same sentence. The Tfreq(wn, W'm)is the number of co- Rw, wm(0)=1 Trecency(wn, Wm)is novelty of the topic. We use Equation 4 to calculate Trecency(wn, Wm). Also, we use the Jaccard coefficient to calculate Rt. The Run wm (t) is the Jaccard mnmn()=lan∩nml and wn at the time t(Equation 5) Proceedings of the 2005 International Workshop on Data Engineering Issues in E-Commerce(DEEC'05) 076952401-X0520.00@2005LEEE SOCIETYFigure 4. An interface for paper recommendation • If the recency of word co-occurrence moves down￾ward, the research topic related to the keywords is an closing. In order to select from papers in a database of Papits, we calculate the topic weight Twn,wm as follows: Twnwm = Tf req(wn, wm) · Trecency(wn, wm) (3) where, wn and wm are words which co-occur in a same sentence. The Tf req(wn, wm) is the number of co￾occurrences in all papers in a database of Papits, and the Trecency(wn, wm) is novelty of the topic. We use Equation 4 to calculate Trecency(wn, wm). Also, we use the Jaccard coefficient to calculate Rt. The Rwnwm(t) is the Jaccard coefficient between wn and wn at the time t (Equation 5). By comparing Rwnwm(t) to Rwnwm (t − 1), we calculate the recency between words wn and wn i.e., the recency of topic moves upward or downward. Trecency(wn, wm) =    Rwnwm (t) Rwnwm(t−1) (Rwnwm (t − 1) = 0) Rwnwm(t) (Rwnwm (t − 1) = 0) Rwnwm(0) = 1 (4) Rwnwm(t) = |wn ∩ wm| |wn ∪ wm| (5) Proceedings of the 2005 International Workshop on Data Engineering Issues in E-Commerce (DEEC’05) 0-7695-2401-X/05 $20.00 © 2005 IEEE