基于Ontology的用户模型研究

针对现有本体用户模型的难点与不足，提出了一种改进的基于领域本体的用户模型（OBUM），利用文本挖掘技术构建领域本体，通过本体学习来完成用户模型的学习和更新。     

Contextual preference mining for user profile construction

The emerging of ubiquitous computing technologies in recent years has given rise to a new field of research consisting in incorporating context-aware preference querying facilities in database systems. One important step in this setting is the Preference Elicitation task which consists in providing the user ways to inform his/her choice on pairs of objects with a minimal effort. In this paper we propose an automatic preference elicitation method based on mining techniques. The method consists in extracting a user profile from a set of user preference samples. In our setting, a profile is specified by a set of contextual preference rules verifying properties of soundness and conciseness. After proving that the problem is NP-complete, we propose a resolution in 2 phases. The first phase extracts all individual user preferences by means of contextual preference rules. The second phase builds the user profile starting from this collection of rules using a greedy method. To assess the quality of user profiles, we propose three ranking techniques benefiting from these profiles that enable us to rank objects according to user preferences. We evaluate the efficacy of our three ranking strategies and compare them with a well-known ranking method (SVMRank). The evaluation is carried out through an extensive set of experiments executed on a real-world database of user preferences about movies.     

Ontology-based mobile augmented reality in cultural heritage sites: information modeling and user study

Augmented reality (AR) has received much attention in the cultural heritage domain as an interactive medium for requesting and accessing information regarding heritage sites. In this study, we developed a mobile AR system based on Semantic Web technology to provide contextual information about cultural heritage sites. Most location-based AR systems are designed to present simple information about a point of interest (POI), but the proposed system offers information related to various aspects of cultural heritage, both tangible and intangible, linked to the POI. This is achieved via an information modeling framework where a cultural heritage ontology is used to aggregate heterogeneous data and semantically connect them with each other. We extracted cultural heritage data from five web databases and modeled contextual information for a target heritage site (Injeongjeon Hall and its vicinity in Changdeokgung Palace in South Korea) using the selected ontology. We then implemented a mobile AR application and conducted a user study to assess the learning and engagement impacts of the proposed system. We found that the application provides an agreeable user experience in terms of its affective, cognitive, and operative features. The results of our analysis showed that specific usage patterns were significant with regard to learning outcomes. Finally, we explored how the study’s key findings can provide practical design guidance for system designers to enhance mobile AR information systems for heritage sites, and to show system designers how to support particular usage patterns in order to accommodate specific user experiences better.

     

基于层次概念的用户兴趣模型研究

使用基于关键词匹配的方法,分析了 HTML 语言描述的Web文档,提取网页中有用的特征信息,得到两类标记中的内容:一类是网页的全局描述信息,如和;另一类起局部修饰作用,强调了网页的部分内容,如.从而提出了基于层次概念的用户模型,并使用向量空间模型方法建立了以突发事件新闻为基础的用户兴趣模型.实验表明,这种方法有一定的可行性.     

Toward finding hidden communities based on user profile

We consider the community detection problem from a partially observable network structure where some edges are not observable. Previous community detection methods are often based solely on the observed connectivity relation and the above situation is not explicitly considered. Even when the connectivity relation is partially observable, if some profile data about the vertices in the network is available, it can be exploited as auxiliary or additional information. We propose to utilize a graph structure (called a profile graph) which is constructed via the profile data, and propose a simple model to utilize both the observed connectivity relation and the profile graph. Furthermore, instead of a hierarchical approach, based on the modularity matrix of the network structure, we propose an embedding approach which utilizes the regularization via the profile graph. Various experiments are conducted over two social network datasets and comparison with several state-of-the-art methods is reported. The results are encouraging and indicate that it is promising to pursue this line of research.     

个性化搜索中的用户特征模型研究

用户特征的描述方式是实现个性化搜索算法的核心因素。针对传统的基于关键词向量空间模型的用户特征描述过于简单,不能全面描述用户兴趣的缺陷,将folksonomy的结构与本体概念的清晰语义相结合,提出一种多层用户特征描述方式。从用户兴趣主题、用户间关联两个不同角度,从用户生成的标签、标记的文档及主题等不同层次建立用户特征描述模型,并将其应用于个性化搜索过程的方式进行分析。同时对个性化搜索的结果评价方式、资源类型对用户特征及搜索结果的影响进行了讨论。在Delicious和Flickr两种不同类型数据集上的实验表明,所提出用户特征模型能够有效提高个性化搜索结果的性能。     

个性化图像检索中用户兴趣模型的构建方法

针对个性化图像检索的语义鸿沟问题,提出了一种新的用户兴趣模型的构建方法。将用户兴趣模型分为长期兴趣和短期兴趣：用户的短期兴趣由图像的低层特征映射得到;用户的长期兴趣经过推理机推理,将短期兴趣映射为高层语义得到,从而弥补语义鸿沟。实验结果表明,经过用户兴趣模型过滤的图像检索结果符合用户的个性化要求,相比已有方法在查准率和查全率上取得了明显的改善。     

一种基于用户配置文件的个性化检索方法*

为了进一步改进个性化搜索方法,通过对现有个性化搜索方法的研究,提出了一种新的搜索方法。该方法从用户兴趣相关性出发,将用户配置文件与传统个性化搜索相结合。在传统TF-IDF方法的基础上,提出了一种综合考虑标签总引用次数和配置文件中标签总数的新方法,用于获取用户配置文件与资源配置文件中的标签权重;设计了基于余弦相似性计算并综合匹配的标签个数的资源相关性计算方法。通过MovieLens数据集试验,验证了本文提出方法的有效性。     

Modelling and learning user preferences over sets

Although there has been significant research on modelling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects. We introduce a representation language, DD-PREF, that balances preferences for particular objects with preferences about the properties of the set. Specifically, we focus on the depth of objects (i.e. preferences for specific attribute values over others) and on the diversity of sets (i.e. preferences for broad vs. narrow distributions of attribute values). The DD-PREF framework is general and can incorporate additional object- and set-based preferences. We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language. We show how preferences represented in DD-PREF can be learned from training data. Experimental results are given for three domains: a blocks world domain with several different task-based preferences, a real-world music playlist collection, and rover image data gathered in desert training exercises.     

Effective spell checking by learning user behavior

This paper describes a spell checking system that learns user behavior. Based on that insight, the system with high likelihood suggests correct replacements for incorrect words and declares unknown, but correct words to be correct. The system relies on three dictionaries, a so-called user history file, and two logic modules to carry out the learning and spell checking. Tests have proved that the system is very fast and highly reliable. Specifically, the top ranked replacement word for an incorrect word was the correct word 96% of the time. Words that were not in the large dictionary but that nevertheless were correct, for example, persons' names, compound words, and control commands, were declared to be correct 82% of the time. It was never observed that an incorrect word was accepted as correct.     

Dynamic adaptation of numerical attributes in a user profile

Recommender systems try to help users in their decisions by analyzing and ranking the available alternatives according to their preferences and interests, modeled in user profiles. The discovery and dynamic update of the users’ preferences are key issues in the development of these systems. In this work we propose to use the information provided by a user during his/her interaction with a recommender system to infer his/her preferences over the criteria used to define the decision alternatives. More specifically, this paper pays special attention on how to learn the user’s preferred value in the case of numerical attributes. A methodology to adapt the user profile in a dynamic and automatic way is presented. The adaptations in the profile are performed after each interaction of the user with the system and/or after the system has gathered enough information from several user selections. We have developed a framework for the automatic evaluation of the performance of the adaptation algorithm that permits to analyze the influence of different parameters. The obtained results show that the adaptation algorithm is able to learn a very accurate model of the user preferences after a certain amount of interactions with him/her, even if the preferences change dynamically over time.     

基于语义事务信息聚类的用户概貌构建

为了揭示用户的访问模式,对传统的基于聚类技术构建用户概貌方法进行了研究,同时引入语义事务分析的观点,提出一种基于潜在语义模型构建用户概貌的方法.通过语义分析中的奇异值分解(SVD)算法,将构建的用户会话-浏览页面矩阵向量空间投影到潜在语义向量空间;利用扩展的K-means聚类算法,对潜在语义向量空间聚类生成用户会话聚类;计算浏览页面均值向量,构建以加权浏览页面集表示的用户概貌;最后采用加权平均访问百分比(WAVP)方法评价构建的用户概貌,表明了该方法的有效性.     

An adaptive user interface based on personalized learning

This adaptive user interface provides individualized, just-in-time assistance to users by recording user interface events and frequencies, organizing them into episodes, and automatically deriving patterns. It also builds, maintains, and makes suggestions based on user profiles.     

Using NLP for machine learning of user profiles

As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles that accurately capture user interest with minimum user interaction. The research focuses on the importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could be drawn from a thesaurus. Our experiments, conducted in the context of a content-based profiling system for on-line newspapers on the World Wide Web (the IDD News Browser), demonstrate the importance of a generalization hierarchy and the promise of combining natural language processing techniques with machine learning (ML) to address an information retrieval (IR) problem.     

Extraction of user profile based on workflow and information flow

A collaborative team usually consists of team members with various domains. These members’ demands for knowledge are also different from each other. For recommending potentially useful knowledge to suitable members, their user profiles should be well managed and maintained. User profile can be input by the members, but a more intelligent way should be the automatic extraction of the user profiles. Workflow and information flow are two types of collaborative processes, which exist behind every collaborative team. This paper is mainly concerned with how to extract these team members’ user profile from the two types of contexts: workflow and information flow. This paper defines a model for the user profile. Then some methods are proposed for extracting the profile information on the basis of workflow and information flow. This study on the user profile extraction can pave the way for developing knowledge recommender systems, which can recommend proper knowledge to proper team members with a collaborative team.     

Matching user identities across social networks with limited profile data

Privacy preservation is a primary concern in social networkswhich employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age, location, education, interests, and others. The task of matching user identities across different social networks is considered a challenging task. In this work, we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data, i.e., user-name and friendship. Thus, we propose a framework, ExpandUIL, that includes three standalone algorithms based on (i) the percolation graph matching in ExpandFullName algorithm, (ii) a supervised machine learning algorithm that works with the graph embedding, and (iii) a combination of the two, ExpandUserLinkage algorithm. The proposed framework as a set of algorithms is significant as, (i) it is based on the network topology and requires only name feature of the nodes, (ii) it requires a considerably low initial seed, as low as one initial seed suffices, (iii) it is iterative and scalable with applicability to online incoming stream graphs, and (iv) it has an experimental proof of stability over a real ground-truth dataset. Experiments on real datasets, Instagram and VK social networks, show upto 75% recall for linked accounts with 96% accuracy using only one given seed pair.     

Personalized Learning Course Planner with E-learning DSS using user profile

Various methods of E-learning systems, based on information and communications, and geared towards improving learning effectiveness and students’ attention span, have been studied. However, most E-learning systems force students to follow the learning course or content established by a teacher. These methods are convenient, but they limit the effectiveness of E-learning.To overcome this limitation and increase effective learning, new techniques that reflect alternative learning styles, such as adaptive learning and personalized learning, have been studied. In this study, we proposed a Personalized Learning Course Planner (PLCP) that allows students to easily select the learning course they desire. User profile data was collected from the students’ initial priorities about learning contents as well as the test scores after their study. E-Learning Decision Support System (EL-DSS) in PLCP suggests an appropriate learning course organization, according to calculated results based on the user profile data.To verify the effectiveness of the proposed system, we implemented an English learning system consisting of PLCP. We conducted an experiment with 30 university students and evaluated students’ satisfaction by questionnaire analysis. The results indicate that the proposed system improved learning effectiveness and student satisfaction. Further investigation of the participants indicated that suggesting a learning course suitable for students’ previous test scores and priorities encouraged students to concentrate on the lesson.     

RLINK: Deep reinforcement learning for user identity linkage

World Wide Web - User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise...