Improved Collaborative Filtering Recommendation Based on Classification and User Trust

When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.

Evaluating Classification Research for Machine Translation Course Teaching

Teaching evaluation can be divided into different types,additionally their functions and applicable conditions are different.According to different standards,teaching evaluation can be divided into different types:(1)according to different evaluation functions,it can be divided into pre-evaluation,intermediate evaluation,and post-evaluation;(2)according to different evaluation reference standards,it can be divided into relative evaluation,absolute evaluation,and individual difference evaluation;(3)according to different evaluation and analysis methods,it can be divided into qualitative and quantitative evaluation;(4)according to the different evaluation subjects,it can be divided into self-evaluation and others’evaluation.This paper introduced research work using different types of teaching evaluation in the machine translation course according to different situations.The research results showed that the rational selection of different types of teaching evaluation methods and the combination of these methods can greatly promote teaching.

Evaluation and reasonable exploitation on the natural view resources of Lushan Mountain scenic spot

The paper carried on the classified and rating evaluation primarily on natural landscape resources in Lushan Mountain. According to the evaluation, exploiting and utilizing the situation of scenic spot natural landscape resources, some reasonable advices were given on further exploiting Lushan Mountain natural scenic spot, expecting that it could supply some theoretical references for the natural landscape resources sustainable development in Lushan Mountain in the future.

Bug Prioritization to Facilitate Bug Report Triage

The large number of new bug reports received in bug repositories of software systems makes their management a challenging task.Handling these reports manually is time consuming,and often results in delaying the resolution of important bugs.To address this issue,a recommender may be developed which automatically prioritizes the new bug reports.In this paper,we propose and evaluate a classification based approach to build such a recommender.We use the Na¨ ve Bayes and Support Vector Machine （SVM） classifiers,and present a comparison to evaluate which classifier performs better in terms of accuracy.Since a bug report contains both categorical and text features,another evaluation we perform is to determine the combination of features that better determines the priority of a bug.To evaluate the bug priority recommender,we use precision and recall measures and also propose two new measures,Nearest False Negatives （NFN） and Nearest False Positives （NFP）,which provide insight into the results produced by precision and recall.Our findings are that the results of SVM are better than the Na¨ ve Bayes algorithm for text features,whereas for categorical features,Na¨ ve Bayes performance is better than SVM.The highest accuracy is achieved with SVM when categorical and text features are combined for training.