基于情境感知的学习资源个性化推荐机制研究

面对海量的学习资源,如何为学习者推荐与情境相匹配的学习资源是亟需解决的问题.文章在详细描述学习资源个性化推荐情境要素的基础上,构建了包含情境感知层、资源管理层、学习诊断层、个性推荐层及学习者界面的学习资源个性化推荐系统,并阐述了系统的推荐流程及实现.在情境感知理论的基础上,构建以情境感知技术为核心的学习资源个性化推荐系...     

ICAMF-CC：改进的情境感知矩阵分解算法

情境感知推荐系统是当前推荐领域的研究热点,而情境感知矩阵分解算法（CAMF-CC）是当前领域的一种有效模型。针对CAMF-CC算法在推荐时存在的准确度不高的问题,提出了一种改进的情境感知矩阵分解算法（ICAMF-CC）。该算法在原有CAMF-CC算法的基础上,通过融入项目偏置项和情境权重来进行评分预测。其优势在于,一方面,融入了项目本身特性对评分的影响;另一方面,充分考虑了不同情境因素在推荐过程中有着不同的影响力,提高了预测评分的准确度。通过在LDOS-CoMoDa数据集上进行实验,结果表明,该算法在准确度上优于CAMF-CC算法。     

移动情境感知及其交互研究

结合情境信息的移动应用和交互研究是普适计算领域中非常重要的内容.综合移动情境感知相关研究的发展,从人机交互研究的角度,提出了基于用户、环境和任务的情境信息分类方法.根据情境信息的数据获取、数据表示、系统架构、数据处理、服务应用和系统评价等六个方面,分析了移动情境感知应用研究的关键问题,总结了移动情境感知对人机交互研究中的研究方法、数据收集、用户控制感及交互方式等产生的影响.最后提出了当前移动情境感知及其交互研究中存在的问题和可能的研究方向.     

情境感知研究现状

移动互联网的迅速普及为情境感知的发展奠定了技术基础,情境感知领域的相关研究逐渐成为了计算机领域里的一个热点,文章将从情境感知的起源开始,介绍情境感知的定义及相关研究方向,梳理出目前情境感知研究的脉络。     

CAUXT:帮助研究人员在感兴趣的情境中采集用户体验数据

随着移动互联技术的快速发展和普及,产品的使用越来越无处不在,这也要求用户体验研究必须与情境紧密关联。但对于用户体验研究,现有的技术手段在识别和感知研究人员感兴趣的情境上还存在一定的困难,较难根据感兴趣的情境获取用户体验数据。其原因在于:现有系统工具的情境感知与用户体验研究人员的情境感知存在较大差异。目前,用户体验领域缺乏解决此类问题的研究,现有相关领域的此类研究也都是倾向于从算法和计算效率的角度来提升系统的数据采集能力,未从用户体验研究人员的情境感知机理角度来解决问题。文中创新性地通过借鉴认知科学和人机交互领域关于人认知的相关理论来构建用户体验研究人员的情境感知模型,并在此基础上利用情境感知计算技术构建具有情境感知能力的用户体验数据采集系统。通过初步的案例研究表明,此系统能够在一定程度上获取用户体验研究人员感兴趣的情境,并根据这些感兴趣的情境获取用户体验数据。     

基于深度学习的专利知识推荐服务研究

专利作为一种包含大多数领域中最完整的设计信息,可以为设计者解决设计问题提供有价值的指导。针对现有的专利推荐方法难以有效地推荐跨领域专利的问题,提出一种基于深度学习的跨领域专利知识推荐方法,用于创新产品的概念设计。对产品功能和知识需求情境进行建模,将设计问题进行标准化表达,生成设计问题空间。提出一种半监督学习算法（TG-TCI）将专利功能信息按照功能基自动分类和标记,利用实体识别算法（BERT-BiLSTM-CRF）提取专利应用场景术语、技术术语,结合国际专利分类（IPC）信息以表示专利的功能、情境、技术和领域属性,从而生成专利知识空间。通过设计问题空间到专利知识空间的功能基和知识情境映射查找所需的跨领域专利,根据技术和领域属性对它们进行聚类和评估,选出特定的专利以激发设计者的创造力。以一个实际案例进行分析验证,证明了基于深度学习的专利知识推荐模型的可行性及有效性。     

基于情境感知的移动阅读个性化推荐服务模式探析

为了满足移动阅读用户在不同阅读情境中的服务需求，提高移动阅读平台的服务质量，文章将情境感知理论融入移动阅读个性化推荐服务中，对基于情境感知的移动阅读个性化推荐服务模式进行探析。首先阐述基于情境感知的移动阅读个性化推荐服务流程，然后构建基于情境感知的移动阅读个性化推荐服务模型，并详细介绍模型中的各个构成模块，最后对如何科学、有效地评估基于情境感知的移动阅读个性化推荐服务效果进行讨论，介绍三类推荐服务效果评估方式。     

基于情境感知的个性化推荐算法

随着互联网的快速发展,只涉及用户和项目的传统个性化推荐已不能满足推荐要求的效率和准确率.因此,情景感知个性化推荐服务引起了广泛关注,成为新的研究热点.本文分析了情境的定义、情景感知个性化推荐模型,并提出了一种基于情境信息降低维度的关联规则推荐模型.最后,以视频网站的web日志为数据源,融合时间情境因素,实现了基于时间情境划分的关联规则推荐算法,并和传统推荐算法进行对比分析,实验证明,情境感知推荐算法具有更高的准确率和召回率.     

可定制的情境感知计算框架原型

分析现存情境感知框架的特点与不足,提出一种面向不同应用领域的可定制的情境感知框架原型,重点阐述原型的体系结构设计和软件运行时基于服务的软件架构。讨论情境感知过程中4个关键问题,包括情境感知系统的可定制化问题、情境规则的迭代与演化、情境感知的交互机制以及情境模式。最后,以该原型为基础,通过在医疗导诊场景下实现情境感知应用验证其实现领域定制的可行性。     

多子域随机森林在情境感知推荐中的应用研究

情境感知推荐系统通过增加情境信息来提高推荐精度,在实际应用中得到广泛的应用。然而,传统的情境感知推荐方法存在赋予情境因素相同权重,忽略了用户在不同情境下所偏好项目的不同,以及情境因素在推荐过程中所起的影响作用不同的问题。提出一种基于多子域随机森林算法的情境感知推荐方法。该方法对特征重要性按权值大小进行排序,将权值的取值区域分为多个大小相等的子区域,在这些子区域中随机选择特征,构造特征子空间来改进随机森林算法;通过改进的随机森林算法来分解并降低用户、项目和情境的特征维度;使用协同过滤推荐算法来进行冷链物流配载个性化推荐。对LDOS-CoMoDa和Cycle Share两个数据集进行仿真实验,结果表明该方法相比传统方法平均绝对误差减少近10%,有效地提高了推荐系统的预测精度,为情境感知推荐的应用提供借鉴。     

A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems

Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users’ demands based on their preference profiles. Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems. CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations. However, the majority of CARSs use ratings as a unique criterion for building communities. Meanwhile, MCRSs utilize user preferences in multiple criteria to better generate recommendations. Up to now, how to exploit context in MCRSs is still an open issue. This paper proposes a novel approach, which relies on deep learning for context-aware multi-criteria recommender systems. We apply deep neural network (DNN) models to predict the context-aware multi-criteria ratings and learn the aggregation function. We conduct experiments to evaluate the effect of this approach on the real-world dataset. A significant result is that our method outperforms other state-of-the-art methods for recommendation effectiveness.     

Progress in context-aware recommender systems — An overview

Recommender Systems are the set of tools and techniques to provide useful recommendations and suggestions to the users to help them in the decision-making process for choosing the right products or services. The recommender systems tailored to leverage contextual information (such as location, time, companion or such) in the recommendation process are called context-aware recommender systems. This paper presents a review on the continual development of context-aware recommender systems by analyzing different kinds of contexts without limiting to any specific application domain. First, an in-depth analysis is conducted on different recommendation algorithms used in context-aware recommender systems. Then this information is used to find out that how these techniques deals with the curse of dimensionality, which is an inherent issue in such systems. Since contexts are primarily based on users’ activity patterns that leads to the development of personalized recommendation services for the users. Thus, this paper also presents a review on how this contextual information is represented (either explicitly or implicitly) in the recommendation process. We also presented a list of datasets and evaluation metrics used in the setting of CARS. We tried to highlight that how algorithmic approaches used in CARS differ from those of conventional RS. In that, we presented what modification or additions are being applied on the top of conventional recommendation approaches to produce context-aware recommendations. Finally, the outstanding challenges and research opportunities are presented in front of the research community for analysis     

A context-aware recommender method based on text and opinion mining

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context-aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context-aware recommendation method based on text mining (CARM-TM). The method includes two techniques to extract context from reviews: CIET.5_embed, a technique based on word embeddings; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, a new technique which extracts context by using aspect-based opinions. We call our extension of CARM-TOM (context-aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context-aware recommender based on matrix factorization. To evaluate CARM-TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM-TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context-aware recommender system.     

Context-aware recommender systems in mobile environment: On the road of future research

Recommender systems have recently been singled out as a fascinating area of research, owing to the technological progress in mobile devices, such as smartphones and tablets, as well as to the rapid growth of social networking. In this respect, the main purpose of recommender systems is to suggest items that help users to make decisions from a large number of possible actions such as what place to visit, what movie to watch, or which friend to add to a social network system. In mobile environment, many personal, social and environmental contextual factors can be integrated into the recommendation process in order to provide the correct recommendation to a special user, at the perfect moment, in the appropriate location based on his/her emotional state, his/her current activity and past behavior. This paper provides an overview of context-aware recommender systems in mobile environment. The objective of this systematic review is to investigate the current state of the art in context-aware recommender systems and classify the reviewed research papers. This study aims equally to identify the possible future directions in this research area.     

A survey of autoencoder-based recommender systems

In the past decade,recommender systems have been widely used to provide users with personalized products and services.However,most traditional recommender systems are still facing a challenge in dealing with the huge volume,complexity,and dynamics of information.To tackle this challenge,many studies have been conducted to improve recommender system by integrating deep learning techniques.As an unsupervised deep learning method,autoencoder has been widely used for its excellent performance in data dimensionality reduction,feature extraction,and data reconstruction.Meanwhile,recent researches have shown the high efficiency of autoencoder in information retrieval and recommendation tasks.Applying autoencoder on recommender systems would improve the quality of recommendations due to its better understanding of users,demands and characteristics of items.This paper reviews the recent researches on autoencoder-based recommender systems.The differences between autoencoder-based recommender systems and traditional recommender systems are presented in this paper.At last,some potential research directions of autoencoder-based recommender systems are discussed.     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.     

Ubiquitous recommender systems

Ubiquitous recommender systems combine characteristics from ubiquitous systems and recommender systems in order to provide personalized recommendations to users in ubiquitous environments. Although not a new research area, ubiquitous recommender systems research has not yet been reviewed and classified in terms of ubiquitous research and recommender systems research, in order to deeply comprehend its nature, characteristics, relevant issues and challenges. It is our belief that ubiquitous recommenders can nowadays take advantage of the progress mobile phone technology has made in identifying items around, as well as utilize the faster wireless connections and the endless capabilities of modern mobile devices in order to provide users with more personalized and context-aware recommendations on location to aid them with their task at hand. This work focuses on ubiquitous recommender systems, while a brief analysis of the two fundamental areas from which they emerged, ubiquitous computing and recommender systems research is also conducted. Related work is provided, followed by a classification schema and a discussion about the correlation of ubiquitous recommenders with classic ubiquitous systems and recommender systems: similarities inevitably exist, however their fundamental differences are crucial. The paper concludes by proposing UbiCARS: a new class of ubiquitous recommender systems that will combine characteristics from ubiquitous systems and context-aware recommender systems in order to utilize multidimensional context modeling techniques not previously met in ubiquitous recommender systems.     

Context-aware recommendation using rough set model and collaborative filtering

Context has been identified as an important factor in recommender systems. Lots of researches have been done for context-aware recommendation. However, in current approaches, the weights of contextual information are the same, which limits the accuracy of the results. This paper aims to propose a context-aware recommender system by extracting, measuring and incorporating significant contextual information in recommendation. The approach is based on rough set theory and collaborative filtering. It involves a three-steps process. At first, significant attributes to represent contextual information are extracted and measured to identify recommended items based on rough set theory. Then the users’ similarity is measured in a target context consideration. Furthermore collaborative filtering is adopted to recommend appropriate items. The evaluation experiments show that the proposed approach is helpful to improve the recommendation quality.     

基于评分矩阵局部低秩假设的成列协同排名算法

协同过滤方法是当今大多数推荐系统的核心.传统的协同过滤方法专注于评分预测的准确性,然而实际推荐系统的推荐结果往往是项目的排序.针对这一问题,将排名学习领域的知识引入推荐算法,设计了一种基于评分矩阵局部低秩假设的成列协同排名算法.选择直接使用计算复杂度较低的成列损失函数来优化矩阵分解模型,并通过实验验证了其在运算速度上的显著提升.在3个实际推荐系统数据集上,与当下主流推荐算法的比较实验结果表明,该算法具有良好的性能.     

Privileged contextual information for context-aware recommender systems

A recommender system is used in various fields to recommend items of interest to the users. Most recommender approaches focus only on the users and items to make the recommendations. However, in many applications, it is also important to incorporate contextual information into the recommendation process. Although the use of contextual information has received great focus in recent years, there is a lack of automatic methods to obtain such information for context-aware recommender systems. Some works address this problem by proposing supervised methods, which require greater human effort and whose results are not so satisfactory. In this scenario, we propose an unsupervised method to extract contextual information from web page content. Our method builds topic hierarchies from page textual content considering, besides the traditional bag-of-words, valuable information of texts as named entities and domain terms (privileged information). The topics extracted from the hierarchies are used as contextual information in context-aware recommender systems. We conducted experiments by using two data sets and two baselines: the first baseline is a recommendation system that does not use contextual information and the second baseline is a method proposed in literature to extract contextual information. The results are, in general, very good and present significant gains. In conclusion, our method has advantages and innovations:(i) it is unsupervised; (ii) it considers the context of the item (Web page), instead of the context of the user as in most of the few existing methods, which is an innovation; (iii) it uses privileged information in addition to the existing technical information from pages; and (iv) it presented good and promising empirical results. This work represents an advance in the state-of-the-art in context extraction, which means an important contribution to context-aware recommender systems, a kind of specialized and intelligent system.