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The purpose of the paper is to conduct an exploratory study that proposes a personalized knowledge integration platform for digital libraries which can provide users with personalized information and knowledge services.

A prototype system (PIKIP_DL) is designed and developed with two types of service, i.e. personalized information/knowledge service and personalized subject category service. Evaluation of the PIKIP_DL by domain specialists and software experts is conducted. Comments are implications are addressed.

The main findings include the following: the proposed system can help suggest materials that readers are interested in for DL; the proposed system can help construct knowledge contents in a hierarchical structure; and a common recommendation concerning knowledge structure from the reviewers is that the proposed system should add a self‐organizing knowledge map function that would allow users to view knowledge subjects in a graphic manner.

The results from the evaluation of reviewers revealed that the proposed PIKIP_DL is acceptable to the integration of both personalized information service and personalized knowledge subject service. This implies that librarians and DL software agents should place emphasis on integrated service development to attract the attention of their users. Towards this goal, they could explain that personalized services (e.g. material recommendation, message recommendation, knowledge subject materials) with a mechanism of multi‐resource integration can help provide DL resources according to users' needs and wants, and in consequence to enhance DL service efficacy.

The research describes the importance of information/knowledge integration with respect to its support on the learning and study methods of users, and has developed a personalized knowledge integration platform as a mechanism that provides a personalized information service and a personalized knowledge subject category service. By employing Apriori algorithm and association rules as the data mining mechanism, personalized information recommendations are derived from circulation data, and a knowledge subject category is integrated from online sharing knowledge by participants.

The purpose of this paper is to study the architecture of holographic personalized portal, user modeling, commodity modeling and intelligent interaction.

In this paper, the authors propose crowd-science industrial ecological system based on holographic personalized portal and its interaction. The holographic personality portal is based on holographic enterprises, commodities and consumers, and the personalized portal consists of accurate ontology, reliable supply, intelligent demand and smart cyberspace.

The personalized portal can realize the information acquisition, characteristic analysis and holographic presentation. Then, the intelligent interaction, e.g. demand decomposition, personalized search, personalized presentation and demand prediction, will be implemented within the personalized portal.

The authors believe that their work on intelligent interaction based on holographic personalized portal, which has been first proposed in this paper, is innovation focusing on the interaction between intelligence and convenience.

This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems.

A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user‐centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system.

The results show that among the proposed UPSC formulas in this paper, the (query‐document)‐graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems.

This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information.

The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.

The purpose of this paper is to provide insights into personalisation from a practitioner’s perspective to bridge the practitioner-academia gap and steer the research agenda. A wide scope of research has investigated personalisation from a consumer perspective. The current study aims at bridging the consumer and practitioner perspective by entering into a dialogue about the practical application of personalisation. It takes the personalisation process model by Vesanen and Raulas (2006) as the starting point.

Lead by the exploratory character of the study, semi-structured expert interviews were conducted with marketers, market researchers and online privacy specialists.

The results showcase how practitioners view the issues present in consumer research. First, they are overly positive about personalisation. Second, they are aware of constraining factors; findings showcase best practices to mitigate them. Finally, practitioners are aware of controversies surrounding personalisation and thus engage in ethical discussions on personalisation.

This study shows that practitioners have somewhat different believes about the utility and appreciation of personalised marketing practices than consumers. It also shows awareness of some of the key concerns of consumers, and that such awareness translates into organisational and technological solutions that can even go beyond what is currently mandated by law. Six insights into personalised marketing as well as expectations for the future of the phenomenon are discussed to steer the research agenda.

Insights into the practice of personalisation contribute to a shared understanding of this phenomenon between involved actors, such as marketers, advertisers, and consumer representatives. In addition, implications for lawmakers are discussed, suggesting that the implementation of privacy laws needs more clarity and that actions aiming at improving consumer knowledge are needed.

The paper contributes to the literature first, by drafting a descriptive map of personalisation from a practitioners’ perspective and contrasting it with the perspective stemming from consumer research and, second, by offering insights into the current developments and direct implications for practice and future research.

The purpose of this paper is to describe the benefits of integrating personalization within a library web site and presents methodology for achieving this goal within an academic setting.

The project documented in this study explores the use of student course enrollment data as the basis for creating a personalized library web site. Off-the-shelf, open source applications are used in conjunction with existing university data to deliver a final product that offers an enhanced user experience for the university community.

Adaptive personalization is increasingly commonplace on the web. Academic libraries have a unique source of existing data that offers the potential of adding personalization to the library web site. At present, the personalization of library online services remains largely unexplored. This project illustrates one relatively low-cost method to help libraries interested in creating personalized web sites.

This paper provides a guide for libraries interested in the implementation of personalization within their web sites.

The project described in this case study is highly unique within libraries. The paper outlines the feasibility and technical requirements associated with using course enrollment data to add personalized content to a library web site.

This paper aims to debate the technology-driven transformation of customer relationship management (CRM) into social CRM, which entails a shift from a transactional and automational solution to a customer experience management philosophy, reflecting high levels of customer empowerment.

A literature review provides a critical analysis of the concept, tenets, aims and implementation approaches of social CRM. Arguments are summarised by developing a process-based framework for implementing social CRM.

By adopting a value co-creation approach that recognises the technology-fostered customer empowerment, the social CRM highlights the need to immigrate from relationship management to relationship stewardship. In this vein, social CRM implementation should support and foster dialogue facilitation and customer engagement in co-creating customer experiences. To achieve these, five approaches for implementing social CRM are proposed: collecting, analysing and interpreting customer insight; monitoring and improving the performance of CRM; developing holistic and seamless personalised customer experiences; gamifying CRM and loyalty programmes; and nurturing community relationship management.

The five approaches to social CRM implementation are identified and validated based on current industry practices, theoretical arguments and anecdotal evidence of professionals’ perceptions about their outcomes. Future research is required to collect hard evidence showing the business and customer impacts of these approaches.

Social CRM immigrates relationship management from a transactional to a customer experience mindset that treats customers as co-creators of value and demands the tourism and hospitality firms to exploit the affordances of information and communication technologies to collect and analyse customer data for better understanding the customer; develop customer touch points that do not only aim to sell but also primarily aim to enhance the customer interactions and experiences; consider and treat the customers and the customer communities as co-creators, brand ambassadors and stewards of relations; and motivate and enable customer participation into value co-creation processes for developing customer experiences and building relationships.

Research in social CRM is emerging, but it mainly focusses on defining its scope and identifying the functionality and adoption of social CRM technology. The paper contributes to the literature by proposing five specific approaches and a process framework for implementing social CRM. Various directions for future research are also provided.

In a previous research, the authors proposed a security information service, called Personalized Real-time Information with Security Map (PRISM), which personalizes the incident information based on living area of individual users. The purpose of this paper is to extend PRISM to conduct sophisticated analysis of street crimes. The extended features enable to look back on past incident information and perform statistical analysis.

To analyze street crimes around living area in more detail, the authors add three new features to PRISM: showing a past heat map, showing a heat map focused on specified type of incidents and showing statistics of incidents for every type. Using these features, the authors visualize the dynamic transition of street crimes in a specific area and the whole region within Kobe city. They also compare different districts by statistics of street crimes.

Dynamical visualization clarifies when, where and what kind of incident occurs frequently. Most incidents occurred along three train lines in Kobe city. Wild boars are only witnessed in a certain region. Statistics shows that the characteristics of street crimes is completely different depending on living area.

Previously, many studies have been conducted to clarify factors relevant to street crimes. However, these previous studies mainly focus on interesting regions as a whole, but do not consider individual’s living area. In this paper, the authors analyze street crimes according to users’ living area using personalized security information service PRISM.

Over the last 50 years, increased attention for personalization paved the way for one-to-one marketing efforts, but firms struggle to deliver on this promise. The purpose of this manuscript is to provide a complete picture on personalization, develop a future research agenda and put forth concrete advice on how to move the field forward from a theoretical, methodological, contextual, and practical viewpoint.

This research follows a systematic literature review process, providing an in-depth analysis of 135 articles (covering 184 studies) to distill the (1) key building blocks and components of personalization and (2) theoretical, contextual, and methodological aspects of the studies.

This manuscript uncovers six personalization components that can be linked to two personalization building blocks: (1) learning: manner, transparency, and timing and (2) tailoring: touchpoints, level, and dynamics. For each of these components, the authors propose future research avenues to stimulate personalization research that accounts for challenges in today's data-rich environments (e.g. data privacy, dealing with new data types). A theoretical, contextual, and methodological (i.e. industry, country and personalization object) review of the selected studies leads to a set of concrete recommendations for future work: account for heterogeneity, embed theoretical perspectives, infuse methodological innovation, adopt appropriate evaluation metrics, and deal with legal/ethical challenges in data-rich environments. Finally, several managerial implications are put forth to support practitioners in their personalization efforts.

This research provides an integration of personalization research beyond existing and outdated review papers. Doing so, it accounts for the impact of new technologies and Artificial Intelligence and aims to advance the next generation of knowledge development on personalization.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

This aim of this article is to propose a technical solution for digital libraries to establish the information needs ontology based on a comprehensive understanding of personalized user information needs and offer an information push service by really simple syndication (RSS) in a ubiquitous computing environment.

First, the authors formalize an ontology model applicable to the ubiquitous computing environment to describe user information needs comprehensively. The information needs related to the user's research activities are regarded as the ontology, which is described with web ontology language (OWL), and thus an ontology knowledgebase is established. Second, in light of the user's explicit information needs, the digital library finds the hidden information needs from the ontology knowledgebase, and also extracts the information resources required by the user from the corresponding database and then pushes them to the user by means of RSS. Finally, users can browse through a man‐machine interface to access the information.

After these steps, an RSS information push service architecture is established based on the user information needs ontology, providing the users with dynamic, personalized and proactive information services.

The innovation of this research lies in the fact that the architecture that is put forward is helpful to improve the accuracy and integrity of information push service of digital library, and is also beneficial for the research on u‐library.