主动学习在通信网络推荐系统中的应用

稀疏网络中大量潜在链接的存在对于链接预测问题是一个很大的挑战。在链接预测任务中引入主动学习,挖掘网络中大量未连接节点对中的潜在信息,从未标记样本中挑选出系统最不确定的样本交由用户判别。获得标记后的样本将给系统较高的信息增益。在通信网络数据集Nodobo中的实验结果表明,使用主动学习之后,该方法为通信用户预测潜在联系人的准确率得到显著的提高。     

User evaluation of a market-based recommender system

Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommender.     

An empirical evaluation of multi-media based learning of a procedural task

The present study investigated the effects of multi-media modules and their combinations on the learning of procedural tasks. In the experiment, 72 participants were classified as having either low- or high spatial ability based on their spatial ability test. They were randomly assigned to one of the six experimental conditions in a 2 × 3 factorial design with verbal modality (on-screen text procedure vs. auditory procedure) and the format of visual representation (static visual representation vs. static visual representation with motion cues vs. animated visual representation). After they completed their learning session, the ability to perform the procedural task was directly measured in a realistic setting. The results revealed that: (1) in the condition of static visual representation, the high spatial ability group outperformed the low spatial ability group, (2) for the low spatial ability participants, the animated visual representation group outperformed the static visual representation group, however, the static visual representation with motion cues group did not outperform the static visual representation group, (3) the use of animated visual representation helped participants with low spatial ability more than those with high spatial ability, and (4) a modality effect was found for the measure of satisfaction when viewing the animated visual representation. Since the participants with low spatial ability benefited from the use of animation, the results might support an idea that people are better able to retrieve the procedural information by viewing animated representation. The findings also might reflect a preference for the auditory mode of presentation with greater familiarity with the type of visual representation.     

Extreme learning machines for credit scoring: An empirical evaluation

Classification algorithms are used in many domains to extract information from data, predict the entry probability of events of interest, and, eventually, support decision making. This paper explores the potential of extreme learning machines (ELM), a recently proposed type of artificial neural network, for consumer credit risk management. ELM possess some interesting properties, which might enable them to improve the quality of model-based decision support. To test this, we empirically compare ELM to established scoring techniques according to three performance criteria: ease of use, resource consumption, and predictive accuracy. The mathematical roots of ELM suggest that they are especially suitable as a base model within ensemble classifiers. Therefore, to obtain a holistic picture of their potential, we assess ELM in isolation and in conjunction with different ensemble frameworks. The empirical results confirm the conceptual advantages of ELM and indicate that they are a valuable alternative to other credit risk modelling methods.     

Utilizing learning process to improve recommender system for group learning support

With the rapid increasing of learning materials and learning objects in e-learning, the need for recommender system has also become more and more imperative. Although, the traditional recommendation system has achieved great success in many domains, it is not suitable to support e-learning recommender system because the approach in e-learning is hybrid and it is obtained mainly by two mechanisms: the learners’ learning processes and the analysis of social interaction. Therefore, this study proposes a flexible recommendation approach to satisfy this demand. The recommendation is designed based on a multidimensional recommendation model. Furthermore, we use Markov Chain Model to divide the group learners into advanced learners and beginner learners by using the learners’ learning activities and learning processes so that we can correctly estimate the rating which also include learners’ social interaction. The experimental result shows that the proposed system can give a more satisfying and qualified recommendation.     

Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation

In this paper, we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during interaction with an environment for exploration-based learning. This work contributes to user modeling and intelligent interfaces research by extending existing research on eye-tracking in HCI to on-line capturing of high-level user mental states for real-time interaction tailoring. We first describe the empirical work we did to understand the user meta-cognitive behaviors to be modeled. We then illustrate the probabilistic user model we designed to capture these behaviors with the help of on-line information on user attention patterns derived from eye-tracking data. Next, we describe the evaluation of this model, showing that gaze-tracking data can significantly improve model performance compared to lower level, time-based evidence. Finally, we discuss work we have done on using pupil dilation information, also gathered through eye-tracking data, to further improve model accuracy.     

An empirical evaluation of P system testing techniques

This paper presents the existing techniques for P system testing and performs an empirical evaluation of their fault-detection efficiency. The comparison is performed using mutation testing and, based on the results obtained, some improved testing methodologies are proposed.     

Deep autoencoders for feature learning with embeddings for recommendations: a novel recommender system solution

Neural Computing and Applications - We propose “Deep Autoencoders for Feature Learning in Recommender Systems,” a novel discriminative model based on the incorporation of features from...     

Teaching requirements elicitation interviews: an empirical study of learning from mistakes

Requirements Engineering - Interviews are the most widely used elicitation technique in requirements engineering (RE). However, conducting a requirements elicitation interview is challenging. The...     

Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system

Collaborative filtering (CF) technique is capable of generating personalized recommendations. However, the recommender systems utilizing CF as their key algorithms are vulnerable to shilling attacks which insert malicious user profiles into the systems to push or nuke the reputations of targeted items. There are only a small number of labeled users in most of the practical recommender systems, while a large number of users are unlabeled because it is expensive to obtain their identities. In this paper, Semi-SAD, a new semi-supervised learning based shilling attack detection algorithm is proposed to take advantage of both types of data. It first trains a naïve Bayes classifier on a small set of labeled users, and then incorporates unlabeled users with EM-λ to improve the initial naïve Bayes classifier. Experiments on MovieLens datasets are implemented to compare the efficiency of Semi-SAD with supervised learning based detector and unsupervised learning based detector. The results indicate that Semi-SAD can better detect various kinds of shilling attacks than others, especially against obfuscated and hybrid shilling attacks.     

An environmental evaluation learning apprentice system

In engineering problems with a weak domain theory, such as environmental evaluation, machine learning techniques should bring to bear any existing background knowledge so as to guide the knowledge-acquisition process. This paper proposes an Interactive Inductive Learning System (IILS) which can use background knowledge provided by experts to avoid incorrect or incomplete induced heuristics. Through the specification of guidance relations, the expert can force the rule-induction system to focus on a subset of relevant attributes and training instances. A guidance relation consists of a set of attribute constraints, where each constraint instructs IILS on the role of an attribute for the induction of a concept. IILS has been linked to the database of an Environmental Evaluation Support System (EESS) for the induction of impact estimation and comparison heuristics. The induced heuristics are generalizations of past assessments stored in the database and permit the prediction of impact levels for cases not previously encountered by EESS. The integration of IILS transforms EESS into an environmental impact assessment learning apprentice system capable of acquiring and improving evaluation heuristics through its normal use.     

An agent-based recommender system for developing customized families of products

Electronic markets and web-based content have improved traditional product development processes by increasing the participation of customers and applying various recommender systems to satisfy individual customer needs. Agent-based systems based on agents’ roles and tasks can provide appropriate tools to solve product design problems by recommending design knowledge and information. This paper introduces an agent-based recommender system to support designing families of products based on customers’ preferences in dynamic electronic market environments. In the proposed system, a market-based learning mechanism is applied to determine the customers’ preferences for recommending appropriate products to customers of the product family. We demonstrate the implementation of the proposed recommender system using a multi-agent framework. Through simulated experiments, we illustrate that the proposed recommender system can help determine the preference values of products for customized recommendation and market segment design in various electronic market environments.     

Towards a conversational language-sensitive system for pascal

This paper discusses the problems involved in constructing a conversational editor for Pascal programs. A review of previous work in this area is given together with an alternative approach.     

iMusic: a session-sensitive clustered classical music recommender system using contextual representation learning

Multimedia Tools and Applications - Music has emerged to be of paramount importance to humanity and is not only considered as a source of entertainment but also an agent that causes social and...     

A visual recommender tool in a collaborative learning experience

Collaborative learning incorporates a social component in distance education to minimize the disadvantages of studying in solitude. Frequent analysis of student interactions is required for assessing collaboration. Collaboration analytics arose as a discipline to study student interactions and to promote active participation in e-learning environments. Unfortunately, researchers have been more focused on finding methods to solve collaboration problems than on explaining the results to tutors and students. Yet if students do not understand the results of collaboration analysis methods, they will rarely follow their advice. In this paper we propose a tool that analyzes student interactions and visually explains the collaboration circumstances to provoke the self-reflection and promote the sensemaking about collaboration. The tool presents a visual explanatory decision tree that graphically highlights student collaboration circumstances and helps to understand the reasoning followed by the tool when prescribing a recommendation. An assessment of the tool has demonstrated: (1) the students collaboration circumstances showed by the tool are easy to understand and (2) the students could realize the possible actions to improve the collaboration process.     

Proposing a charting recommender system for second-language nurses

This paper proposes a recommender system to assist second-language nurses, especially student nurses, in patient care charting. Using data mining association rules, the system automatically provides optimal words or terms for the nurse to choose from as she or he proceeds with the documentation. The system, having been tested on Taiwanese nursing students who needed to chart in their practicum at hospitals, has been found to be highly effective for the intended purpose.     

e‐Learning recommender system for a group of learners based on the unified learner profile approach

In the age of information explosion, e‐learning recommender systems (eL_RSs) have emerged as effective information filtering techniques that attempt to provide the most appropriate learning resources for learners while using e‐learning systems. These learners are differentiated on the basis of their learning styles, goals, knowledge levels and others. Several attempts have been made in the past to design eL_RSs to recommend resources to individuals; however, an investigation of recommendations to a group of learners in e‐learning is still in its infancy. In this paper, we focus on the problem of recommending resources to a group of learners rather than to an individual. The major challenge in group recommendation is how to merge the individual preferences of different learners that form a group and extract a pseudo unified learner profile (ULP) that closely reflects the preferences of all learners. Firstly, we propose a profile merging scheme for the ULP by utilizing learning styles, knowledge levels and ratings of learners in a group. Thereafter, a collaborative approach is proposed based on the ULP for effective group recommendations. Experimental results are presented to demonstrate the effectiveness of the proposed group recommendation strategy for e‐learning.