Complex adaptive filtering user profile using graphical models

This article explores how to develop complex data driven user models that go beyond the bag of words model and topical relevance. We propose to learn from rich user specific information and to satisfy complex user criteria under the graphical modelling framework. We carried out a user study with a web based personal news filtering system, and collected extensive user information, including explicit user feedback, implicit user feedback and some contextual information. Experimental results on the data set collected demonstrate that the graphical modelling approach helps us to better understand the complex domain. The results also show that the complex data driven user modelling approach can improve the adaptive information filtering performance. We also discuss some practical issues while learning complex user models, including how to handle data noise and the missing data problem.     

Personalised news and scientific literature aggregation

Content-based filtering can be deployed for personalised information dissemination on the web, but this is a possibility that has been largely ignored. Nowadays, there are no successful content-based filtering applications available online. Nootropia is an immune-inspired user profiling model for content-based filtering. It has the advantageous property to be able to represent a user’s multiple interests and adapt to a variety of changes in them. In this paper we describe our early efforts to develop real world personalisation services based on Nootropia. We present, the architecture, implementation, usage and evaluation of the personalised news and paper aggregator, which aggregates news and papers that are relevant to an individual’s interests. Our user study shows that Nootropia can effectively learn a user’s interests and identify relevant information. It also indicates that information filtering is a complicated task with many factors affecting its successful application in a real situation.     

A rough-fuzzy document grading system for customized text information retrieval

Due to the large repository of documents available on the web, users are usually inundated by a large volume of information, most of which is found to be irrelevant. Since user perspectives vary, a client-side text filtering system that learns the user's perspective can reduce the problem of irrelevant retrieval. In this paper, we have provided the design of a customized text information filtering system which learns user preferences and modifies the initial query to fetch better documents. It uses a rough-fuzzy reasoning scheme. The rough-set based reasoning takes care of natural language nuances, like synonym handling, very elegantly. The fuzzy decider provides qualitative grading to the documents for the user's perusal. We have provided the detailed design of the various modules and some results related to the performance analysis of the system.     

基于信息过滤的Web信息查询优化

从信息过滤的角度分析信息查询的个性化发展,通过用户需求与信息内容的相似性匹配过滤与用户需求无关的信息,从而实现网络环境下用户信息查询结果的优化.据此建立基于信息过滤的用户模型框架,探讨基于信息过滤的信息查询系统优化实现的过程和方法.     

融合用户画像与协同过滤的知识付费平台个性化推荐模型

[目的/意义]为提高知识付费平台用户感知服务质量,文章构建了融合用户画像与协同过滤的个性化推荐模型。[方法/过程]首先根据用户特性构建画像标签体系,利用TF-IDF、熵值法、k-means等方法确定用户特征标签;其次分别基于用户画像与改进后的协同过滤算法计算用户相似度,通过调和权重得到用户综合相似度;最后利用Top-N进行个性化推荐。[结果/讨论]通过知乎live付费用户信息进行验证,发现本文算法在推荐结果的准确率以及召回率上,相比其单一方法均有较大提升,且满意度高于知乎live平台。     

基于内容的智能网络多媒体信息过滤检索

The paper discusses the construction of a content-based intelligent system that performs multimedia information filtering and retrieving on the Internet. The system disassembles the multimedia information into different media objects and describes them with vectors for content-based retrieval. In the user study module, the system uses the BP neural network to clarify the user interests for intelligent filtering and retrieving.     

Filtering search results using an optimal set of terms identified by an artificial neural network

Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user’s interest (a user profile) with similar sets of terms representing the data items. Many techniques can be employed for constructing user profiles automatically, but they usually yield large sets of term. Various dimensionality-reduction techniques can be applied in order to reduce the number of terms in a user profile. We describe a new terms selection technique including a dimensionality-reduction mechanism which is based on the analysis of a trained artificial neural network (ANN) model. Its novel feature is the identification of an optimal set of terms that can classify correctly data items that are relevant to a user. The proposed technique was compared with the classical Rocchio algorithm. We found that when using all the distinct terms in the training set to train an ANN, the Rocchio algorithm outperforms the ANN based filtering system, but after applying the new dimensionality-reduction technique, leaving only an optimal set of terms, the improved ANN technique outperformed both the original ANN and the Rocchio algorithm.     

Group event recommendation based on graph multi-head attention network combining explicit and implicit information

In event-based social networks (EBSN), group event recommendation has become an important task for groups to quickly find events that they are interested in. Existing methods on group event recommendation either consider just one type of information, explicit or implicit, or separately model the explicit and implicit information. However, these methods often generate a problem of data sparsity or of model vector redundancy. In this paper, we present a Graph Multi-head Attention Network (GMAN) model for group event recommendation that integrates the explicit and implicit information in EBSN. Specifically, we first construct a user-explicit graph based on the user's explicit information, such as gender, age, occupation and the interactions between users and events. Then we build a user-implicit graph based on the user's implicit information, such as friend relationships. The incorporated both explicit and implicit information can effectively describe the user's interests and alleviate the data sparsity problem. Considering that there may be a correlation between the user's explicit and implicit information in EBSN, we take the user's explicit vector representation as the input of the implicit information aggregation when modeling with graph neural networks. This unified user modeling can solve the aforementioned problem of user model vector redundancy and is also suitable for event modeling. Furthermore, we utilize a multi-head attention network to learn richer implicit information vectors of users and events from multiple perspectives. Finally, in order to get a higher level of group vector representation, we use a vanilla attention mechanism to fuse different user vectors in the group. Through experimenting on two real-world Meetup datasets, we demonstrate that GMAN model consistently outperforms state-of-the-art methods on group event recommendation.     

Information extraction with automatic knowledge expansion

POSIE (POSTECH Information Extraction System) is an information extraction system which uses multiple learning strategies, i.e., SmL, user-oriented learning, and separate-context learning, in a question answering framework. POSIE replaces laborious annotation with automatic instance extraction by the SmL from structured Web documents, and places the user at the end of the user-oriented learning cycle. Information extraction as question answering simplifies the extraction procedures for a set of slots. We introduce the techniques verified on the question answering framework, such as domain knowledge and instance rules, into an information extraction problem. To incrementally improve extraction performance, a sequence of the user-oriented learning and the separate-context learning produces context rules and generalizes them in both the learning and extraction phases. Experiments on the “continuing education” domain initially show that the F1-measure becomes 0.477 and recall 0.748 with no user training. However, as the size of the training documents grows, the F1-measure reaches beyond 0.75 with recall 0.772. We also obtain F-measure of about 0.9 for five out of seven slots on “job offering” domain.     

Measuring the success of the Greek Taxation Information System

The transformation of many governments all around the world into new forms, namely, electronic government (e-Government), could not leave the Greek government unaffected. Therefore, it initiated an e-Government project related to national information systems and finance services, the Greek Taxation Information System (TAXIS). The purpose of this paper is to investigate the success of TAXIS from the perspective of expert employees, who work in public taxation agencies. This topic is interesting, because TAXIS is applied in a tax-driven country, under a mandatory setting. Also, it is the first time that the success of this project is examined, from the perspective of employees, using IS success models. The study adapts DeLone and McLean [DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten year update. Journal of Management Information Systems, 19(4), 9–30] and Seddon's [Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3) 240–253] information systems success models. The model developed includes the constructs of information, system and service quality, perceived usefulness and user satisfaction. The results provide evidence that there are strong connections between the five success constructs. All hypothesized relationships are supported, except for the relationship between system quality and user satisfaction. The empirical evidence and discussion presented can help the Greek Government improve and fully exploit the potential of TAXIS as an innovative tool for taxation purposes.     

Instance-based cognitive mapping: a process for discovering a knowledge worker's tacit mental model

This paper addresses tacit-to-explicit knowledge externalization, arguably the most critical, and yet problematic, phase of Nonaka's knowledge creation theory. Specifically, we propose and describe instance-based cognitive mapping (ICM), a unique externalization process that analyzes multiple decision instances using the inductive learning algorithms of artificial intelligence to generate a polynomial representation of the knowledge worker's mental model, explicitly relating how the knowledge worker implicitly selects and weighs key factors in making decisions within a specific problem domain. After reviewing current externalization techniques, we describe the characteristics, and evaluate the advantages, of the ICM process. An exploratory test of the process suggests that inductive learning algorithms, such as the group method of data handling, can be used to discover a reasonable polynomial estimate of a knowledge worker's tacit mental model. This estimate can then be compared with other explicit models and standards, updated with new information and knowledge, and internalized by all interested knowledge workers.     

基于协同过滤的高校图书馆纸本资源智能推荐方法实证研究

[目的/意义]旨在为高校图书馆提高纸质资源采购质量与利用率提供参考。[方法/过程]基于用户画像的理论对不同用户进行多维度的刻画,利用机器学习中监督学习的方法,通过采用协同过滤的推荐算法对用户偏好特征做精细统计分析的定量化计算,并从用户需求的角度建立用户偏好同步变化的自适应优化在线学习的纸本资源推荐系统。[结果/结论]该研究从实证分析角度为用户实现精准的个性化纸本资源推荐服务,为高校图书馆纸质文献检索库实现智能偏好的检索功能,建立纸质文献检索库合理有效的动态更新机制,提升用户体验。     

Building user profiles based on sequences for content and collaborative filtering

Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using different sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include different parameters to control the efficiency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent (δ-matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm.We evaluate our approach with several state-of-the-art recommendation algorithms using different evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach offers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited.     

The 'Self-service' Student: Building Enterprise-wide Systems into Universities 1

This article investigates the take-up by universities of enterprise-wide computer systems and the development of a new module for the management and administration of students. Having its origins in Electronic Commerce, the system assumes the existence of a certain kind of user, one with particular roles and responsibilities--Aa self-service user. The notion of 'self-service' is deployed as an integral part of the system rollout where students are to view, input and modify administrative and financial information on themselves and their courses. Drawing from the sociology of science and technology, and material from a 3-year ethnographic study, we describe the system's implementation in a British university. While accepting of the need for an ERP system the campus community reject self-service. However, as we will show, because Campus Management is a 'global product' unwanted functionality can be difficult to resist outright and this can have important implications for the autonomy of the university and the reshaping of fundamental principles and relationships.     

专题文献过滤系统中的用户模板构建技术研究

用户模板的构建是信息过滤系统建设的最重要的工作之一。本文首先介绍几种用户兴趣模型的构建技术，然后阐述在我们研制的专题文献过滤系统中所采用的用户模板构建方法。     

Hybrid collaborative filtering model for consumer dynamic service recommendation based on mobile cloud information system

The rapid development of the web has led to a considerable increase in information dissemination. Recently, personalized web service recommendation has become a popular research area in service computing. Research on web service recommendation systems mainly addresses two problems: prediction and completion of sparse QoS data, and the user's personalized recommendation. To address the issue of high data sparsity and low recommendation accuracy in the traditional service recommendation models under mobile cloud, this study presents a hybrid collaborative filtering model for consumer service recommendation based on mobile cloud by introducing user preferences. The example verified that the service recommendation based on the model can effectively reduce the data sparsity and increase the accuracy of the prediction.     

基于信息协同过滤的电子政务推荐服务系统研究

采用信息协同过滤技术构建一个面向公众的电子政务信息推荐服务系统框架。通过信息用户评价矩阵寻找相似度较高的邻居集,能够较好地把握用户的需求偏好,主动提供适合用户的信息组合。但需要解决稀疏性、冷启动、扩展瓶颈问题,以及处理好用户参与、个人隐私和系统优化问题。