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This study aims to examine the difference between personalized and non-personalized recommendations in influencing YouTube users’ video choices. In addition, whether men and women have a significant difference in using recommendations was compared and the predictors of recommendation video use frequency were explored.

A survey of 524 Saudi Arabia college students was conducted using computer-assisted self-administered interviews to collect their video recommendation sources and how likely they follow the recommendation from different sources.

Video links posted on social media used by the digital natives were found as the most effective form of recommendation shows that social approval is important in influencing trials. Recommendations can succeed in both personalized and non-personalized ways. Personalized recommendations as in YouTube recommended videos are almost the same as friends and family’s non-personalized posting of video links on social media in convincing people to watch the videos. Contrary to expectations, Saudi men college students are more likely to use recommendations than women students.

The use of a non-probability sample is a major limitation and self-reported frequency may result in over- or under-estimation of video use.

Marketers will realize that they may not need the personalized recommendation from the large site. They can use social media recommendations by the consumers’ friends and family. E-mail is the worst platform for a recommendation.

Recommendation is a credible source and can overcome the avoidance of advertising. Its influence on consumers will be increasing in years to come with the algorithmic recommendation and social media use.

This is the first study to compare the influence of different online recommendation sources and compare personalized and non-personalized recommendations. As recommendation is growing more and more important with algorithm development online, the study results have high reference values to marketers in Islamic countries and beyond.

The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.

This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.

The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.

This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

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.

This chapter reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies – the Direct method, the Inverse Propensity Score (IPS) estimator, and the Doubly Robust (DR) method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.

The purpose of this paper is an exploratory study of customers’ “lived” experiences of commercial recommendation services to better understand customer expectations for personalization with recommendation agents. Recommendation agents programmed to “learn” customer preferences and make personalized recommendations of products and services are considered a useful tool for targeting customers individually. Some leading service firms have developed proprietary recommender systems in the hope that personalized recommendations could engage customers, increase satisfaction and sharpen their competitive edge. However, personalized recommendations do not always deliver customer satisfaction. More often, they lead to dissatisfaction, annoyance or irritation.

The critical incident technique is used to analyze customer satisfactory or dissatisfactory incidents collected from online group discussion participants and bloggers to develop a classification scheme.

A classification scheme with 15 categories is developed, each illustrated with satisfactory incidents and dissatisfactory incidents, defined in terms of an underlying customer expectation, typical instances of satisfaction and dissatisfaction and, when possible, conditions under which customers are likely to have such an expectation. Three pairs of themes emerged from the classification scheme. Six tentative research propositions were introduced.

Findings from this exploratory research should be regarded as preliminary. Besides, content validity of the categories and generalizability of the findings should be subject to future research.

Research findings have implications for identifying priorities in developing algorithms and for managing personalization more strategically.

This research explores response to personalization from a customer’s perspective.

This paper aims to find a way to personalize attraction recommendations for travelers. The research objective is to find a more accurate way to suggest new attractions to each traveler based on the opinions of other like-minded travelers and the traveler’s preferences.

To achieve the goal, developers have created a personalized system to generate attraction recommendations. The system considers an individual traveler’s preferences to construct a qualitative attraction ranking model. The new ranking model is the result of blending two processes: K-means clustering and the analytic hierarchy process (AHP).

The performance of the developed recommendation system has been assessed by measuring the accuracy and scalability of the ranking model of the system. The experimental results indicate that the ranking model always returns accurate results independent of the number of attractions and the number of travelers in each cluster. The ranking model has also proved to be scalable because the processing time is independent of the numbers of travelers. Additionally, the results reveal that the overall system usability is at a very satisfactory level.

The main theoretical implication is that integrating the processes of K-means and AHP techniques enables a new qualitative ranking model for personalized recommendations that deliver only high-quality attractions. However, the designed recommendation system has some limitations. First, it is necessary to manually update information about the new tourist attractions. Second, the overall response time depends on the internet bandwidth and latency.

This research contributes to the tourism business and individual travelers by introducing an accurate and scalable way to suggest new attractions to each traveler. The potential benefit includes possible increased revenue for travel agencies that offer personalized package tours and support individual travelers to make the final travel decisions. The designed system could also integrate with itinerary planning systems to plot out a journey that pinpoints what travelers will most enjoy.

This research proposes a design and implementation of a personalized recommendation system based on the qualitative attraction ranking model introduced in this article. The novel ranking model is designed and developed by integrating K-means and AHP techniques, which has proved to be accurate and scalable.

本研究主要探索如何建立个性化旅游胜地推荐模型。本研究通过分析旅游兴趣相似的游客意见和游客偏好选择, 建立一种更加准确推荐游客需要的旅游胜地方法。

为了达到研究目的, 本研究建立了一种个性化推荐旅游胜地的信息系统。其系统通过分析每个游客的旅游偏好来建设一种定性旅游胜地排名模型。这种新型模型主要通过结合以下两种分析算法：（1）K平均聚类算法（K-means clustering）（2）层次分析法（AHP）。

本研究建立的推荐信息系统经过了准确率和拓展性的测评。实验结果表明这种排名模型的准确率并不受旅游胜地多少和游客样本大小的影响。此外, 这种排名模型也具有拓展性, 因为算法时间并不受游客样本大小的影响。最后, 研究实验表明此排名模型客户体验性达到合格满意要求。

本研究的主要理论意义在于其结合了K平均聚类算法和层次分析法, 并建立了一种新型定性排名模型, 这种排名模型个性化地推荐更高质量的旅游胜地给游客。然而, 这种推荐信息系统有一些局限性。第一, 新旅游胜地的信息需要手动输入。第二, 整个系统的处理时间决定于网络带宽和延迟状况。

本研究的实践意义在于其建立了一种准确和具有拓展性的新型旅游胜地推荐模型。这种模型的潜在价值将有利于旅游机构提供定制化旅游套餐和帮助游客制定旅游计划。此外, 这种模型还可以结合旅游路线计划系统以制定更加使游客满意的旅游行程。

本研究推荐了一种基于定性旅游胜地排名模型的个性化旅游推荐模型。这种新型的排名模型结合K平均聚类算法和层次分析法, 实验证明这种模型更具准确性和拓展性。

The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e‐services, in particular, business partner recommendation e‐services for Australian small to medium enterprises (SMEs).

The study first proposes a product semantic relevance model. It then develops a hybrid semantic recommendation approach which combines item‐based collaborative filtering (CF) similarity and item‐based semantic similarity techniques. This hybrid approach is implemented into an intelligent business‐partner‐locator recommendation‐system prototype called BizSeeker.

The hybrid semantic recommendation approach can help overcome the limitations of existing recommendation techniques. The recommendation system prototype, BizSeeker, can recommend relevant business partners to individual business users (e.g. exporters), which therefore will reduce the time, cost and risk of businesses involved in entering local and international markets.

The study would be of great value in e‐government personalization research. It would facilitate the transformation of the current G2B e‐services into a new stage wherein the e‐government agencies offer personalized e‐services to business users. The study would help government policy decision‐makers to increase the adoption of e‐government services.

Providing personalized e‐services by e‐government can be seen as an evolution of the intentions‐based approach and will be one of the next directions of government e‐services. This paper develops a new recommender approach and systems to improve personalization of government e‐services.

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

The ability to acquire and process consumer information online has provided web‐based vendors with the ability to personalize their merchandising at a very low cost. However, empirically establishing the expected positive effect of personalized merchandising has been difficult for practical as well as financial reasons. The aim of this paper is to compare the effectiveness of personalized vs random merchandising on consumers' attitudes and behaviors.

A longitudinal subject experiment comparing standardized vs personalized merchandising was adopted. A fictitious web site was created for the purposes of the study.

Personalized items led to more clicks than random suggestions. Moreover, a positive attitude towards personalization enhanced the attitude towards the web site.

Even if credibility was enhanced thanks to the web site design, the research suffered from a lack of external validity. Additionally, the procedure prevented us from observing any potential effect on basket size.

A strategy of personalizing the content appeared to be relevant for web site managers. They should use “close” recommendations rather than “broad” recommendations and present a moderate number of personalized suggestions.

The research is one of the few online experiments with a longitudinal perspective, which is considered necessary when studying consumers' reactions to the personalization “process”.