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The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.

By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop.

The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision.

Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

Tags help promote customer engagement on video-sharing platforms. Video tag recommender systems are artificial intelligence-enabled frameworks that strive for recommending precise tags for videos. Extant video tag recommender systems are uninterpretable, which leads to distrust of the recommendation outcome, hesitation in tag adoption and difficulty in the system debugging process. This study aims at constructing an interpretable and novel video tag recommender system to assist video-sharing platform users in tagging their newly uploaded videos.

The proposed interpretable video tag recommender system is a multimedia deep learning framework composed of convolutional neural networks (CNNs), which receives texts and images as inputs. The interpretability of the proposed system is realized through layer-wise relevance propagation.

The case study and user study demonstrate that the proposed interpretable multimedia CNN model could effectively explain its recommended tag to users by highlighting keywords and key patches that contribute the most to the recommended tag. Moreover, the proposed model achieves an improved recommendation performance by outperforming state-of-the-art models.

The interpretability of the proposed recommender system makes its decision process more transparent, builds users’ trust in the recommender systems and prompts users to adopt the recommended tags. Through labeling videos with human-understandable and accurate tags, the exposure of videos to their target audiences would increase, which enhances information technology (IT) adoption, customer engagement, value co-creation and precision marketing on the video-sharing platform.

The proposed model is not only the first explainable video tag recommender system but also the first explainable multimedia tag recommender system to the best of our knowledge.

Hotel classification systems are used to convey information about facilities and services. Yet, they have been prone to criticism for overemphasizing facilities at the expense of other matters of importance to service quality. In contrast, online travel agents (OTAs) use innovative methods to evaluate satisfaction with hotels. Conventional systems will lose relevance if they do not step up to consider service aspects associated with customer satisfaction. This chapter probes five hotel classification systems along with one OTA and leverages the literature to propose an improved framework classification. This is based on nine critical areas that include service quality, infrastructure, facilities and services, human resources, sustainability, safety and security, accessibility, quality systems, and online hotel ratings.

The purpose of this paper is to determine how best to reduce, reuse and dispose of household waste medicines in the National Health Service (NHS) (UK).

Through a combination of literature review and empirical work, this research investigates the existing household waste medicines reverse logistics (RL) system and makes recommendations for improvement by benchmarking it against household waste batteries RL. The viability and feasibility of these recommendations are evaluated through in-depth interviews with healthcare professionals and end user surveys.

The batteries RL system appears to be a more structured and effective system with more active engagement from actors/stakeholders in instigating RL practices and for this very reason is an excellent comparator for waste medicines RL practices. Appropriate best practices are recommended to be incorporated into the waste medicines RL system, including recapturing product value, revised processing approaches, system cooperation and enforcement, drivers and motivations and system design and facilitation.

This study offers academics and professionals an improved insight into the current household waste medicines RL system and provides a step towards reducing an existing gap in this under-researched area. A limitation is that only a small sample of healthcare professionals were involved in subjectively evaluating the feasibility of the recommendations, so the applicability of the recommendations needs to be tested in a wider context and the cost effectiveness of implementing the recommendations needs to be analysed.

Reducing, reusing and properly disposing of waste medicines contribute to economic sustainability, environmental protection and personal and community safety. The information retrieved from analysing returned medicines can be used to inform prescribing practice so as to reduce unnecessary medicine waste and meet the medicine optimisation agenda.

This paper advocates learning from best practices in batteries RL to improve the waste medicines RL design and execution and supports the current NHS agenda on medicine waste reduction (DoH, 2012). The recommendations made in the paper not only aim to reduce medicine waste but also to use medicines effectively, placing the emphasis on improving health outcomes.

Systems to support literature review (LR) and manuscript preparation tend to focus on only one or two of the tasks involved. The purpose of this paper is to describe an intervention framework that redesigns a particular set of tasks, allowing for interconnectivity between the tasks and providing appropriate user interface display features for each task in a prototype system.

A user evaluation study was conducted on the prototype system. The system supports the three tasks: building a reading list (RL) of research papers, finding similar papers based on a set of papers and shortlisting papers from the final RL for inclusion in manuscript based on article type. A total of 119 researchers who had experience in authoring research papers, participated in the evaluation study. They had to select one of the provided 43 topics and execute the tasks offered by the system. Three questionnaires were provided for evaluating the tasks and system. Both quantitative and qualitative analyses were performed on the collected evaluation data.

Task redesign aspects had a positive impact in user evaluation for the second task of finding similar papers while improvement was found to be required for the first and third tasks. The tasks interconnectivity features seed basket and RL were helpful for the participants in conveniently searching for papers within the system. Two of the four proposed informational display features, namely, information cue labels and shared co-relations were the most preferred features of the system. Student user group found the task recommendations and the overall system to be more useful and effective than the staff group.

This study validates the importance of interconnected task design and novel informational display features in accentuating task-based recommendations for LR and manuscript preparatory tasks. The potential for improvement in recommendations was shown through the task redesign exercise where new requirements for the tasks were identified. The resultant prototype system helps in bridging the gap between novices and experts in terms of LR skills.

Encouraging socio‐economic development in developing countries has resulted in many changes in the lifestyle of communities. Changes in dietary patterns are one of the main outcomes from the rapid socio‐economics advancement, for example excessive intake of fat, high‐protein diet (animal protein), salt and preservatives. Chronic diseases such as diabetes, coronary artery disease, hypertension and cancer are mostly related to diet. With the community becoming more nutrition and health conscious, one of the challenges faced is to make sure that the information and knowledge on diet and healthy lifestyle gets across to the community. This paper presents a model of web‐based diet system (WebDIET) that attempts to make diet information and menu plans that are customised to local preference more accessible via the use of Internet. The system is to be used by dieticians who serve as administrators and the public who are the end users. The dietary standard adapted in developing the system is Recommended Dietary Allowances (RDA) for Malaysia. The Malaysian Dietary Guidelines was also referred as it emphasises on Malaysian diet. The system consists of six modules namely Authentication Module, Menu Plan Module, Diabetic Menu Plan Module, Food Selection Module, Disease Info Module and Feedback Module. Diabetic menu plan module models the reasoning process employed by dieticians in suggesting menu plans. The planning task is solved using an artificial intelligence technique through the case‐based reasoning (CBR) approach. CBR, generally describes, the process of solving the current problem based on the proposed solution of similar problems in the past. Nearest Neighbour Algorithm was used to compute the similarities in weighted average. Tools used for the development of the system are Microsoft Visual Interdev, Microsoft FrontPage 2000, while HTML, VBScript and JavaScript are the scripting languages used to develop the system.

A good recommender system helps users find items of interest on the web and can provide recommendations based on user preferences. In contrast to automatic technology‐generated recommender systems, this paper aims to use dynamic expert groups that are automatically formed to recommend domain‐specific documents for general users. In addition, it aims to test several effectiveness measures of rank order to determine if the top‐ranked lists recommended by the experts were reliable.

In the approach, expert groups evaluate web documents to provide a recommender system for general users. The authority and make‐up of the expert group are adjusted through user feedback. The system also uses various measures to gauge the difference between the opinions of experts and those of general users to improve the evaluation effectiveness.

The proposed system is efficient when there is major support from experts and general users. The recommender system is especially effective where there is a limited amount of evaluation data from general users.

This is an original study of how to effectively recommend web documents to users based on the opinions of human experts. Simulation results were provided to show the effectiveness of the dynamic expert group for recommender systems.

The purpose of this paper is to propose a fourfold semantic similarity that results in more accuracy compared to the existing literature. The change detection in the URL and the recommendation of the source documents is facilitated by means of a framework in which the fourfold semantic similarity is implied. The latest trends in technology emerge with the continuous growth of resources on the collaborative web. This interactive and collaborative web pretense big challenges in recent technologies like cloud and big data.

The enormous growth of resources should be accessed in a more efficient manner, and this requires clustering and classification techniques. The resources on the web are described in a more meaningful manner.

It can be descripted in the form of metadata that is constituted by resource description framework (RDF). Fourfold similarity is proposed compared to three-fold similarity proposed in the existing literature. The fourfold similarity includes the semantic annotation based on the named entity recognition in the user interface, domain-based concept matching and improvised score-based classification of domain-based concept matching based on ontology, sequence-based word sensing algorithm and RDF-based updating of triples. The aggregation of all these similarity measures including the components such as semantic user interface, semantic clustering, and sequence-based classification and semantic recommendation system with RDF updating in change detection.

The existing work suggests that linking resources semantically increases the retrieving and searching ability. Previous literature shows that keywords can be used to retrieve linked information from the article to determine the similarity between the documents using semantic analysis.

These traditional systems also lack in scalability and efficiency issues. The proposed study is to design a model that pulls and prioritizes knowledge-based content from the Hadoop distributed framework. This study also proposes the Hadoop-based pruning system and recommendation system.

The pruning system gives an alert about the dynamic changes in the article (virtual document). The changes in the document are automatically updated in the RDF document. This helps in semantic matching and retrieval of the most relevant source with the virtual document.

The recommendation and detection of changes in the blogs are performed semantically using n-triples and automated data structures. User-focussed and choice-based crawling that is proposed in this system also assists the collaborative filtering. Consecutively collaborative filtering recommends the user focussed source documents. The entire clustering and retrieval system is deployed in multi-node Hadoop in the Amazon AWS environment and graphs are plotted and analyzed.

The purpose of this paper is to construct the linkage between libraries and up-to-date news. This study developed a system to recommend libraries’ resources to those daily news readers who are interested in the topics of the target news. The analysis of experiments results served as the reference for the development and improvement of linking libraries’ resources with other web resources.

Up-to-date news were gathered through the news feeds to make the integration with the libraries’ records. In task 1, the libraries’ records were linked and recommended to the target libraries’ records which are of the same topics. In task 2, the system aimed to find the relevant libraries’ records for target news. Three recommendation methods were compared in both tasks to find the most effective approach to the system.

Experiment results showed that: at first, in task 1, the system can assign the libraries’ records of the related topics effectively; second, in task 2, the recommending system can obtain a satisfied recall hit rate through human evaluation. Therefore, regarding the popularity of the daily news online, the linkage and recommendation with the libraries’ resources can increase the visibility of the libraries’ resources and eventually promote the information consuming in libraries.

The authors have confirmed, using three matrix factorization methods, that weighted matrix factorization used in the libraries’ records recommendation system, could achieve better performance than the other two. Based on the research, the libraries could incorporate the online news and libraries’ resources in practice.

To increase the visibility and promote information consuming of libraries, this study proposed a novel method to construct the linkage between library and up-to-date news. The results of data analysis indicate that recommendation of libraries resources through the daily news can achieve effective performance. Thus, it can be inferred that the research results of this study are representative and have practical values in real world practice.

As researchers are writing a draft paper with incomplete structure or text, one of burdensome tasks is to deliberate about which references should be cited for one sentence or paragraph of this draft. In view of the rapid increase in the number of research papers, researchers desire to figure out a better way to do citation recommendations in developing their draft papers. The purpose of this paper is to propose citation recommendation algorithms that enable the acquisition of relevant citations for research papers that are still at the drafting stage. This study attempts to help researchers to select appropriate references among the vast amount of available papers and make draft papers complete in reference citation.

This study adopts a model for recommending citations for incomplete drafts. Four algorithms are proposed in this study. The first and second algorithms are unsupervised models, applying term frequency-inverse document frequency and WordNet technologies, respectively. The third and fourth algorithms are based on the second algorithm to integrate different weight adjustment strategies to improve performance.

The proposed recommendation method adopts three techniques, including using WordNet to transform vector and setting adjustment weights according to structural factors and the information completeness degree, to generate citation recommendation for incomplete drafts. The experiments show that all these three techniques can significantly improve the recommendation accuracy.

None of the methods employed in previous studies can recommend articles as references for incomplete drafts. This paper addresses the situation that a draft paper can be incomplete either in structure or text or both. Recommended references, however, can be still generated and inserted into any desired sentence of the draft paper.