A group recommendation system for online communities

Online communities are virtual spaces over the Internet in which a group of people with similar interests or purposes interact with others and share information. To support group activities in online communities, a group recommendation procedure is needed. Though there have been attempts to establish group recommendation, they focus on off-line environments. Further, aggregating individuals’ preferences into a group preference or merging individual recommendations into group recommendations—an essential component of group recommendation—often results in dissatisfaction of a small number of group members while satisfying the majority. To support group activities in online communities, this paper proposes an improved group recommendation procedure that improves not only the group recommendation effectiveness but also the satisfaction of individual group members. It consists of two phases. The first phase was to generate a recommendation set for a group using the typical collaborative filtering method that most existing group recommendation systems utilize. The second phase was to remove irrelevant items from the recommendation set in order to improve satisfaction of individual members’ preferences. We built a prototype system and performed experiments. Our experiment results showed that the proposed system has consistently higher precision and individual members are more satisfied.     

Iterative rule-guided reasoning over sparse knowledge graphs with deep reinforcement learning

In recent years, reasoning over knowledge graphs (KGs) has been widely adapted to empower retrieval systems, recommender systems, and question answering systems, generating a surge in research interest. Recently developed reasoning methods usually suffer from poor performance when applied to incomplete or sparse KGs, due to the lack of evidential paths that can reach target entities. To solve this problem, we propose a hybrid multi-hop reasoning model with reinforcement learning (RL) called SparKGR, which implements dynamic path completion and iterative rule guidance strategies to increase reasoning performance over sparse KGs. Firstly, the model dynamically completes the missing paths using rule guidance to augment the action space for the RL agent; this strategy effectively reduces the sparsity of KGs, thus increasing path search efficiency. Secondly, an iterative optimization of rule induction and fact inference is designed to incorporate global information from KGs to guide the RL agent exploration; this optimization iteratively improves overall training performance. We further evaluated the SparKGR model through different tasks on five real world datasets extracted from Freebase, Wikidata and NELL. The experimental results indicate that SparKGR outperforms state-of-the-art baseline models without losing interpretability.     

Knowledge-aware sequence modelling with deep learning for online course recommendation

The recent boom in online courses has necessitated personalized online course recommendation. Modelling the learning sequences of users is key for course recommendation because the sequences contain the dynamic learning interests of the users. However, current course recommendation methods ignore heterogeneous course information and collective sequential dependency between courses when modelling the learning sequences. We thus propose a novel online course recommendation method based on knowledge graph and deep learning which models course information via a course knowledge graph and represents courses using TransD. It then develops a bidirectional long short-term memory network, convolutional neural network, and multi-layer perceptron for learning sequence modelling and course recommendation. A public dataset called MOOCCube was used to evaluate the proposed method. Experimental results show that: (1) employing the course knowledge graph in learning sequence modelling improves averagely the performance of our method by 13.658%, 16.42%, and 15.39% in terms of HR@K, MRR@K, and NDCG@K; (2) modelling the collective sequential dependency improves averagely the performance by 4.11%, 6.37%, and 5.47% in terms of the above metrics; and (3) our method outperforms popular methods with the course knowledge graph in most cases.     

An adaptive online learning algorithm for distributed convex optimization with coupled constraints over unbalanced directed graphs

This paper investigates a distributed optimization problem over multi-agent networks subject to both local and coupled constraints in a non-stationary environment, where a set of agents aim to cooperatively minimize the sum of locally time-varying cost functions when the communication graphs are time-changing connected and unbalanced. Based on dual decomposition, we propose a distributed online dual push-sum learning algorithm by incorporating the push-sum protocol into dual gradient method. We then show that the regret bound has a sublinear growth of O(T^p) and the constraint violation is also sublinear with order of $O\left(T^{1?p/2}\right),$ where T is the time horizon and 0 < p ≤ 1/2. Finally, simulation experiments on a plug-in electric vehicle charging problem are utilized to verify the performance of the proposed algorithm. The proposed algorithm is adaptive without knowing the total number of iterations T in advance. The convergence results are established on more general unbalanced graphs without the boundedness assumption on dual variables. In addition, more privacy concerns are guaranteed since only dual variables related with coupled constraints are exchanged among agents.     

A rough set-based association rule approach for a recommendation system for online consumers

Increasing use of the Internet gives consumers an evolving medium for the purchase of products and services and this use means that the determinants for online consumers’ purchasing behaviors are more important. Recommendation systems are decision aids that analyze a customer's prior online purchasing behavior and current product information to find matches for the customer's preferences. Some studies have also shown that sellers can use specifically designed techniques to alter consumer behavior. This study proposes a rough set based association rule approach for customer preference analysis that is developed from analytic hierarchy process (AHP) ordinal data scale processing. The proposed analysis approach generates rough set attribute functions, association rules and their modification mechanism. It also determines patterns and rules for e-commerce platforms and product category recommendations and it determines possible behavioral changes for online consumers.     

在线科技合作社区的一个计算机支持系统

随着互联网的飞速发展,在线科技合作社区大量涌现,并在科学研究与技术开发中日益发挥重要作用。本文从在线社区集体智能的概念模型出发,提出一个针对在线科技合作社区的计算机支持系统框架,促进社区人员网络和知识网络的演化。本文所提的框架以社会网络站点和知识门户为基础,综合多项Web/Web 2.0技术,为在线科技合作社区提供较为全面的支持。     

网络视角下集群企业知识转移和学习路径

产业集群是一种典型的网络组织形式,集群企业之间的知识转移和组织学习是其竞争优势的重要来源。关系网络为集群企业之间的知识转移提供了工具和媒介。基于网络情境中知识创造和知识转移的互动和协调机制考察,发现集群企业存在两条典型的知识学习轨迹。     

Collaborative filtering with facial expressions for online video recommendation

Online video recommender systems help users find videos suitable for their preferences. However, they have difficulty in identifying dynamic user preferences. In this study, we propose a new recommendation procedure using changes of users’ facial expressions captured every moment. Facial expressions portray the users’ actual emotions about videos. We can utilize them to discover dynamic user preferences. Further, because the proposed procedure does not rely on historical rating or purchase records, it properly addresses the new user problem, that is, the difficulty in recommending products to users whose past rating or purchase records are not available. To validate the recommendation procedure, we conducted experiments with footwear commercial videos. Experiment results show that the proposed procedure outperforms benchmark systems including a random recommendation, an average rating approach, and a typical collaborative filtering approach for recommendation to both new and existing users. From the results, we conclude that facial expressions are a viable element in recommendation.     

Reinforcement learning with dynamic completion for answering multi-hop questions over incomplete knowledge graph

Text-enhanced and implicit reasoning methods are proposed for answering questions over incomplete knowledge graph (KG), whereas prior studies either rely on external resources or lack necessary interpretability. This article desires to extend the line of reinforcement learning (RL) methods for better interpretability and dynamically augment original KG action space with additional actions. To this end, we propose a RL framework along with a dynamic completion mechanism, namely Dynamic Completion Reasoning Network (DCRN). DCRN consists of an action space completion module and a policy network. The action space completion module exploits three sub-modules (relation selector, relation pruner and tail entity predictor) to enrich options for decision making. The policy network calculates probability distribution over joint action space and selects promising next-step actions. Simultaneously, we employ the beam search-based action selection strategy to alleviate delayed and sparse rewards. Extensive experiments conducted on WebQSP, CWQ and MetaQA demonstrate the effectiveness of DCRN. Specifically, under 50% KG setting, the Hits@1 performance improvements of DCRN on MetaQA-1H and MetaQA-3H are 2.94% and 1.18% respectively. Moreover, under 30% and 10% KG settings, DCRN prevails over all baselines by 0.9% and 1.5% on WebQSP, indicating the robustness to sparse KGs.     

Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data

Detecting sentiments in natural language is tricky even for humans, making its automated detection more complicated. This research proffers a hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data. It reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual (written text) and visual (still images) and their combination within the online content using decision level multimodal fusion. The proposed contextual ConvNet-SVM_BoVW model, has four modules, namely, the discretization, text analytics, image analytics, and decision module. The input to the model is multimodal text, m ε {text, image, info-graphic}. The discretization module uses Google Lens to separate the text from the image, which is then processed as discrete entities and sent to the respective text analytics and image analytics modules. Text analytics module determines the sentiment using a hybrid of a convolution neural network (ConvNet) enriched with the contextual semantics of SentiCircle. An aggregation scheme is introduced to compute the hybrid polarity. A support vector machine (SVM) classifier trained using bag-of-visual-words (BoVW) for predicting the visual content sentiment. A Boolean decision module with a logical OR operation is augmented to the architecture which validates and categorizes the output on the basis of five fine-grained sentiment categories (truth values), namely ‘highly positive,’ ‘positive,’ ‘neutral,’ ‘negative’ and ‘highly negative.’ The accuracy achieved by the proposed model is nearly 91% which is an improvement over the accuracy obtained by the text and image modules individually.     

A social recommender mechanism for improving knowledge sharing in online forums

Nowadays, online forums have become a useful tool for knowledge management in Web-based technology. This study proposes a social recommender system which generates discussion thread and expert recommendations based on semantic similarity, profession and reliability, social intimacy and popularity, and social network-based Markov Chain (SNMC) models for knowledge sharing in online forum communities. The advantage of the proposed mechanism is its relatively comprehensive consideration of the aspects of knowledge sharing. Accordingly, results of our experiments show that with the support of the proposed recommendation mechanism, requesters in forums can easily find similar discussion threads to avoid spamming the same discussion. In addition, if the requesters cannot find qualified discussion threads, this mechanism provides a relatively efficient and active way to find the appropriate experts.     

A recommender mechanism for social knowledge navigation in an online encyclopedia

In today’s world, knowledge is important for constructing core competitive advantages for individuals and organizations. Recently, Web 2.0 applications and social media have provided a convenient medium for people to share knowledge over the Internet. However, the huge amount of created knowledge can also leads to the problem of information overload. This research proposes a social knowledge navigation mechanism that utilizes the techniques of relevant knowledge network construction, knowledge importance analysis, and knowledge concept ontology construction to generate a visualized recommendation of a knowledge map of sub-concept and knowledge of an article reading sequence for supporting learning activities related to a free online encyclopedia. The results of experiments conducted on Wikipedia show that the proposed mechanism can effectively recommend useful articles and improve a knowledge seeker’s learning effectiveness.     

A neural knowledge graph evaluator: Combining structural and semantic evidence of knowledge graphs for predicting supportive knowledge in scientific QA

Effectively detecting supportive knowledge of answers is a fundamental step towards automated question answering. While pre-trained semantic vectors for texts have enabled semantic computation for background-answer pairs, they are limited in representing structured knowledge relevant for question answering. Recent studies have shown interests in enrolling structured knowledge graphs for text processing, however, their focus was more on semantics than on graph structure. This study, by contrast, takes a special interest in exploring the structural patterns of knowledge graphs. Inspired by human cognitive processes, we propose novel methods of feature extraction for capturing the local and global structural information of knowledge graphs. These features not only exhibit good indicative power, but can also facilitate text analysis with explainable meanings. Moreover, aiming to better combine structural and semantic evidence for prediction, we propose a Neural Knowledge Graph Evaluator (NKGE) which showed superior performance over existing methods. Our contributions include a novel set of interpretable structural features and the effective NKGE for compatibility evaluation between knowledge graphs. The methods of feature extraction and the structural patterns indicated by the features may also provide insights for related studies in computational modeling and processing of knowledge.     

A knowledge schema for organisational learning in academic libraries

This article introduces a formal methodology for deriving conceptual knowledge schema for today's academic libraries. This knowledge schema is defined in the form of a set of knowledge structures and their relationships, and with the purpose of identifying organisational learning requirements. These schemas will then form basis for an organisational knowledge base that assists collaborating librarians to identify appropriate links to relevant knowledge sources within the context of the tasks that they perform. The article demonstrates that the proposed schema when used in conjunction with a specialised knowledge map called the Awareness Net will constitute a suitable conceptual framework for identifying knowledge sharing and organisational learning requirements in today's university libraries.     

Using the taxonomy of cognitive learning to model online searching

In this research, we investigated whether a learning process has unique information searching characteristics. The results of this research show that information searching is a learning process with unique searching characteristics specific to particular learning levels. In a laboratory experiment, we studied the searching characteristics of 72 participants engaged in 426 searching tasks. We classified the searching tasks according to Anderson and Krathwohl’s taxonomy of the cognitive learning domain. Research results indicate that applying and analyzing, the middle two of the six categories, generally take the most searching effort in terms of queries per session, topics searched per session, and total time searching. Interestingly, the lowest two learning categories, remembering and understanding, exhibit searching characteristics similar to the highest order learning categories of evaluating and creating. Our results suggest the view of Web searchers having simple information needs may be incorrect. Instead, we discovered that users applied simple searching expressions to support their higher-level information needs. It appears that searchers rely primarily on their internal knowledge for evaluating and creating information needs, using search primarily for fact checking and verification. Overall, results indicate that a learning theory may better describe the information searching process than more commonly used paradigms of decision making or problem solving. The learning style of the searcher does have some moderating effect on exhibited searching characteristics. The implication of this research is that rather than solely addressing a searcher’s expressed information need, searching systems can also address the underlying learning need of the user.     

ARL: An adaptive reinforcement learning framework for complex question answering over knowledge base

Recently, reinforcement learning (RL)-based methods have achieved remarkable progress in both effectiveness and interpretability for complex question answering over knowledge base (KBQA). However, existing RL-based methods share a common limitation: the agent is usually misled by aimless exploration, as well as sparse and delayed rewards, leading to a large number of spurious relation paths. To address this issue, a new adaptive reinforcement learning (ARL) framework is proposed to learn a better and interpretable model for complex KBQA. First, instead of using a random walk agent, an adaptive path generator is developed with three atomic operations to sequentially generate the relation paths until the agent reaches the target entity. Second, a semantic policy network is presented with both character-level and sentence-level information to better guide the agent. Finally, a new reward function is introduced by considering both the relation paths and the target entity to alleviate sparse and delayed rewards. The empirical results on five benchmark datasets show that our model is more effective than state-of-the-art approaches. Compared with the strong baseline model SRN, the proposed model achieves performance improvements of 23.7% on MetaQA-3 using the metric Hits@1.     

农产品网货品牌培育节俭式创新路径研究

品牌建设贯穿农业全产业链，是推动农业农村高质量发展的持久动力，但发端于网购、成长于网购的特色农产品网货品牌还较少受到学者关注。本文采用双案例研究方法，首先提出了整合品牌定位理论、品牌叙事理论和电商运营理论的农产品网货品牌培育节俭式创新理论框架，然后利用研究假设对“夔山里二娃子”和“小七陈卤”两个来自草根创业的农产品网货品牌进行了案例分析研究。研究发现互联网嵌入为农产品网货品牌节俭式创新创造了机遇，精准定位帮助农产品网货品牌占领消费者心智，品牌故事是农产品网货品牌与消费者之间的情感纽带，体验口碑是农产品网货品牌持续成长的动力。本文的结论丰富了网货品牌培育机理的理论认识，对于区域农产品电商产业发展、农产品电商草根创业者如何建立“大品牌”实践路径具有一定参考价值。     

基于组织学习的知识动态传播模型

组织为了保持其竞争优势和创新能力必须开展组织学习。组织学习首先发生在非正式团体中，通过非正式团体中个体间的知识传播，最后传播到正式团体并最终成为组织知识。本文在分析并提出必要假设基础上，给出一个数学模型，探讨了知识传播的高峰期及其影响因素，为组织管理者促进知识的高效传播提供理论依据。     

学习成本与技术学习的路径选择——基于中国光纤光缆产业技术学习的经济学考察

中国光纤光缆产业正向技术学习失败与逆向技术学习成功的经济学分析表明,学习成本决定技术学习的有效路径;分阶段渐进式学习成本之和小于一次性学习的总成本;社会有没有承担必要的学习成本,从根本上决定一个国家或地区技术学习的整体状况;而成功学习的技术跨度,则取决于特定环境下企业与社会所能承担的学习成本大小。