如何寫標題

1、用一句話概括你所做的工作；
2、字數忌長（盡可能不要超過20字）；
3、考慮搜索引擎的影響，包含關鍵詞。

4、例子
例子1：Enhancing slope one recommendation through local information embedding (目的：enhance the performance of an existing algorithm；技術：local information embedding)
例子2：Sentiment based matrix factorization with reliability for recommendation (目的： reliability for recommendation；技術： sentiment based matrix factorization)

如何寫摘要

1、幾句話概括你所做的工作；

2、用語要簡單，最好讓外行也能看懂；

3、盡量避免把所有細節都說清楚，盡量避免用很專業的術語來描述，盡量避免出現數學符號。

例子1：英文摘要寫法（十句）
(1) The description of the area and the problem. XXX is an important issue in XXX.
(2) Existing works. There are some?
(3) The drawback/limitation of the existing works. However, ...
(4) The content of the paper. In this paper, we propose ...
(5)~(7) Detailed explanation of our work. First, .... Second,.... Third, ....
(8)~(9) Experimental results ...
(10) The significance of the paper (maybe unnecessary).

例子2：

Recommender systems guide their users in decisions related to personal opinions about items.?Sentence 1, the problem statement.?Most existing systems implicitly assume the recommender behavior as a binary classification.?Sentence 2, existing work.?That is, the incoming item is either recommended or not.?Sentence 3, limitation of existing work.?In this paper, we propose a framework integrating three-way decision and random forest to build recommender systems.?Sentence 4, the main content.?First, we consider both misclassification costs and teacher cost.?Sentence 5, the first technique.?Misclassification costs are paid for wrong recommendation behaviors, while teacher cost is paid to consult the user actively for her tendency.?Sentence 5', more about the first technique.?Second, with these costs, a three-way decision model is built and rational settings of $\alpha^*$ and $\beta^*$ are computed.?Sentence 6, the second technique.?Third, we build a random forest to compute the probability $P$ that a user likes an item.?Sentence 7, the third technique.?Fourth, $\alpha^*$, $\beta^*$ and $P$ are employed for determining the recommender behavior to users.?Sentence 7', the fourth technique.?The performance of the recommender is evaluated by the average cost.?Sentence 7', more about the main content.?Experiments results on the well-known MovieLens dataset show that the threshold pair (,) determined by three-way decision is optimal not only on the training set, but also on the testing set.?Sentences 8 and 9, the experimental setting and the result.

如何寫引言

1、比題目和摘要更進一步，用幾段話說清你的工作；
2、要點是充分論證你所做工作的必要性和重要性，要讓審稿人認同并迫不及待想往下看；
3、行文邏輯嚴密，論證充分：
（1）說明問題是什么；
（2）目前最好的工作面臨什么挑戰；
（3）我們的方法能緩解上述挑戰。

4、例子：摘要擴充為引言

(1) Recommender systems attempt to guide users in decisions related to choosing items based on inferences about their personal opinions. Most existing systems implicitly assume the underlying classi?cation is binary, that is, a candidate item is either recommended or not.

(2) Here we propose an alternate framework that integrates three-way decision and random forests to build recommender systems.?

(3)?With these costs, a three-way decision model is built, and rational settings for positive and negative threshold values and? are computed.?

(4)?We next construct a random forest to compute the probability that a user will like an item.

(5)?Finally, , , and are used ?to determine the recommender’s behavior.

重要寫作工具

1、LaTex

（1）強烈建議?LaTex代替Word；

（2）http://www.ctex.org/HomePage

2、Bibtex

（1）?動?成參考?獻列表

3、MetaPost

（1）編程畫?量圖

參考文獻

[1]?如何做研究、如何寫論文，周志華，南京大學

[2]?機器翻譯學術論?文寫作?方法和技巧，劉洋，清華大學

[3]?張恒汝，閔帆，Three-way recommender systems based on random forests, Knowledge-Based Systems, 2016, 91: 275-286.

[4]?張恒汝，閔帆，石兵，Regression-based three-way recommendation，Information Sciences, 2017, 378, 444-461.

[5]?沈蓉萍，張恒汝，于洪，閔帆，Sentiment based matrix factorization with reliability for recommendation. Expert Systems with Applications, 2019.

[6]?張恒汝, 閔帆，吳彥學, 付卓林, 高磊, Magic barrier estimation models for recommended systems under normal distribution, Applied Intelligence, 2018: 1-8.

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