By An Liu, Yoshiharu Ishikawa, Tieyun Qian, Sarana Nutanong, Muhammad Aamir Cheema
DASFAA is an annual overseas database convention, positioned within the Asia-Pacific region,which exhibit situations cutting-edge R & D actions in databases-terms and their functions. It presents a discussion board for technical shows and discussions between database researchers, builders and clients from academia, company and undefined. DASFAA 2015 the 20 th within the sequence, used to be held in the course of April 20-23, 2015 in Hanoi, Vietnam. during this yr, we conscientiously chosen workshops, each one concentrating on particular study concerns that give a contribution to the most topics of the DASFAA convention. This quantity includes the ultimate types of papers authorized for the 2 workshops: moment foreign Workshop on Semantic Computing and Personalization (SeCoP 2015); moment foreign Workshop on titanic information administration and repair (BDMS 2015); and a Poster consultation.
[All the workshops have been chosen through a public call-for-proposals approach. The workshop organizers positioned a massive volume of attempt into soliciting and - lecting papers with a stability of top of the range, new principles and new purposes. We requested all workshops to stick with a inflexible paper choice method, together with the strategy to make sure that any application Committee participants are excluded from the paper evaluation strategy of any paper they're concerned with. a demand in regards to the total paper recognition price of not more than 50% was once additionally imposed on the entire workshops.]
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Extra info for Database Systems for Advanced Applications: DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Hanoi, Vietnam, April 20-23, 2015, Revised Selected Papers
32 3 Y. Cai et al. e. their learning status in the past, and then, to analyze students’ learning trends based on their learning in the past and predict students future performances and achievements according to the trends. Therefore, the problem studied in this paper can be divided into two sub-problems: Estimating Students’ Mastery of Knowledge. To solve this sub-problem, we need to collect students’ behavioral data of learning in the learning process, build appropriate estimating model, estimate students’ mastery of knowledge components they have learned, which can be used to analyze students’ learning trend and predict students’ performance.
But depth-ﬁrst search is used and the users whose depth smaller than a threshold are trusted in this algorithm. TrustWalker  is an algorithm in which random walk method is used to search trusted people in trust network. In all these algorithms there is one thing in common and it is searching trusted people in trust network. These trusted people are actually opinion leaders and their behavior or comments become the reference of other users in the network. But when users rate one item, they will make a conclusion not only according to the comment of opinion leaders, but also according to their own experience.
We obtain 47,064 ratings, assigned by 965 users on 6730 items. Each user has rated at least 2 items, and the ratings follow the 1 (bad) to 5 (excellent) numerical scale. 9928. And there are 47,064 reviews, of the data set is 1 − 965×6,730 which can be used to extract user preference. 2 Evaluation Metrics To measure the predicting accuracy, we use the eﬀective metric mean absolute error (MAE), which is deﬁned as the average absolute diﬀerence between predicting ratings and actual ratings. It is computed by the following formula.