基于大规模数据集用户评论的协作过性能的实验研究
Sumaia Al-Ghuribi1,2, Shahrul Azman Mohd Noah1, Mawal Mohammed3
1Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
PeerJ. Computer science
|September 14, 2023
概括
本研究通过使用用户评论来增强协作过 (CF) 建议. 新方法利用情绪分析和方面情绪对来提高隐性评级准确性,提高推性能.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 信息检索 信息检索
背景情况:
- 协作过 (CF) 依赖于用户对推的明确评分.
- 在许多领域,明确的评级往往稀少或不可用.
- 用户评论为隐含反提供了有价值的数据来源.
研究的目的:
- 通过从用户评论中获得隐性评分来提高CF性能.
- 解决现有方法的局限性,这些方法忽视了情绪度和审查方面.
- 提出计算隐性评级的新方法,以获取更丰富的审查信息.
主要方法:
- 开发了从用户评论中计算隐性评级的四种方法.
- 方法包括情感词度和方面情感词对.
- 结合隐式评级和显式评级,用于增强的CF算法.
主要成果:
- 拟议的隐性评级方法显著提高了CF评级预测的准确性.
- 在大规模的亚马逊和Yelp数据集上评估方法.
- 在三个预测准确度指标中表现优于传统的明确评级.
结论:
- 利用情绪分析和从用户评论中提取方面来增强CF.
- 新的隐性评级计算方法为显式评级提供了强大的替代方案.
- 这种方法可以提高推系统的准确性和有效性.
相关概念视频
Data Collection by Experiments
24.3K
Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
An example of the experimental method is a public...
24.3K
Pharmacokinetic Models: Comparison and Selection Criterion
103
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
103
Cluster Sampling Method
12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Sample Size Calculation
3.4K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.4K
Analysis of Population Pharmacokinetic Data
291
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
291
Aggregates Classification
344
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
344


