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With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.

Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods.

The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M.

This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

The purpose of this paper is to study what extent readers’ socio-demographic characteristics, literary preferences and search behavior predict success in fiction search in library catalogs.

In total, 80 readers searched for interesting novels in four differing search tasks. Their search actions were recorded with a Morae Recorder. Pre- and post-questionnaires elicited information about their background, literary preferences and search experience. Readers’ literary preferences were grouped into four orientations by a factor analysis. Linear regression analysis was applied for predicting search success as measured by books’ interest scores.

Most literary orientations contributed to search success, but in differing search tasks. The role of result examination was greater compared to querying in contributing search success almost in each task. The proportion of variance explained in books’ interest scores varied between 5 (open-ended browsing) and 50 percent (analogy search).

The distribution of participants was biased toward females, and the results are aggregated within search session, both reducing the variation of the phenomenon observed.

This study is one of the first to explore how readers’ literary preferences and searching are associated with finding interesting novels, i.e. search success, in library catalogs. The results expand and support the findings in Mikkonen and Vakkari (2017) concerning associations between reader characteristics and fiction search success.

The purpose of this article is to investigate the factors that explain the reasons why customers may be willing to use chatbots in Zimbabwe as an e-banking customer service gateway, an area that remains under researched.

The research study applied a cross-sectional survey of 430 customers from five selected commercial banks conducted in Harare, the capital city of Zimbabwe. Hypotheses were tested using structural equation modelling.

The research study showed that a counterintuitive intention to use chatbots is directly affected by chatbots' expected performance, the habit of using them and other factors.

To better appreciate the current research concept, there is a need to replicate the same study in other contexts to enhance generalisability.

Chatbots are a trending new technology and are starting to be increasingly adopted by banks and they have to consider that customers need to get used to them.

This study contributes to bridging the knowledge gap as it investigates the factors that explain why bank customers may be willing to use chatbots in five selected commercial Zimbabwean banks. This is a pioneering study in the context of a developing economy such as Zimbabwe.

Many recommender systems are generally unable to provide accurate recommendations to users with limited interaction history, which is known as the cold-start problem. This issue can be resolved by trivial approaches that select random items or the most popular one to recommend to the new users. However, these methods perform poorly in many cases. This paper aims to explore the problem that how to make accurate recommendations for the new users in cold-start scenarios.

In this paper, the authors propose embedded-bandit method, inspired by Word2Vec technique and contextual bandit algorithm. The authors describe user contextual information with item embedding features constructed by Word2Vec. In addition, based on the intelligence measurement model in Crowd Science, the authors propose a new evaluation method to measure the utility of recommendations.

The authors introduce Word2Vec technique for constructing user contextual features, which improved the accuracy of recommendations compared to traditional multi-armed bandit problem. Apart from this, using this study’s intelligence measurement model, the utility also outperforms.

Improving the accuracy of recommendations during the cold-start phase can greatly raise user stickiness and increase user favorability, which in turn contributes to the commercialization of the app.

The algorithm proposed in this paper reflects that user contextual features can be represented by clicked items embedding vector.

The visitor economy of Forks now clearly relies upon a niche form of tourism – as fans of The Twilight Saga are drawn to the setting and filming location of the films. The purpose of this study is to consider the process of diversification and subsequently present recommendations that could inform a future diversification strategy for Forks, in preparation for a post-film tourism scenario.

The research methods employed in this study have two interlinked but distinct elements. Firstly, the Twilight Effect in Forks (WA, USA) is considered as an illustrative case study to shed light on the issues facing a destination that has seen a tourism boom as a direct result of popular culture – The Twilight Saga Franchise. Secondly, a scenario thinking and planning approach is applied when considering the “long-view” future of tourism in Forks.

This article presents a post-film tourism future scenario for Forks; it suggests tourism diversification and a shift towards cultural heritage and wellness. Forks is well placed to afford such tourism experiences, as it offers unique cultural and natural characteristics; furthermore, these could be utilised to create and maintain a distinctive destination image. In doing so a more socially and environmentally sustainable industry can be established, one which supports the local community, including the Quileute tribe.

The article offers original discussions within the film-tourism literature with novel approaches to understanding the management and pre-planning opportunities for destinations that have become popular film tourism locations, with the application of a “Tourism Diversification Model”. The model is adapted from Ansoff Matrix and can be applied as a framework in future studies exploring destination diversification. The investigation of Forks as a post-film tourism case study alone is unique, and the discussions and findings presented are original.

This article focuses on how the film, Harry Potter and the Sorcerer’s Stone (Columbus, 2001; Rowling, 1998), can be used to teach the concepts related to team leadership. In addition, the article offers a discussion of the student and professor perspectives on using film in the classroom and provides recommendations for implementation.

We applied Tuckman and Jensen’s (1977) stages of small group development to frame a vicarious learning experience utilizing the movie, Harry Potter and the Sorcerer’s Stone. This approach is grounded in experiential learning, guiding learners through a shared experience, reflection, conceptualization, and experimentation.

Popular culture artifacts (PCA) can be used to transport learners to a context within which they can vicariously experience leadership concepts that might otherwise be abstract.

Intentional preparation and facilitation can result in engaged, effective leadership learning through film.

A few months from the time of this survey, the nearly completed inpatient psychiatric facility within the Aminu Kano Teaching Hospital's complex would be ready for admissions. Understanding the health workers' level of experience of mental illness and their likely behavioural responses towards people with psychiatric illness, therefore, should be a good baseline to understanding their likely reactions towards admitting such patients within a general hospital setting.

The study, which used a pre-tested and adapted attribution questionnaire, was pro -spective and cross-sectional. Randomly selected health workers in Aminu Kano Teaching Hospital had their level of familiarity and attributions towards psychiatric patients assessed.

The respondents showed a high level of experience with mental illness, with more than 3 in 5 of them having watched movies on mental illness before. More than half of them held positive (favorable) attributions towards persons with mental illness on nine of the ten assessed attribution factors. Almost all held negative (unfavourable) opinion towards intimate relationships with such persons. Attribution factors, “Responsibility, “Anger”, “Dangerousness”, “Fear” and “Segregation” were significantly related to the respondents' level of education (P<0.05). Marital status of the respondents related significantly to “Pity” and “Avoidance” factors (P<0.05). Having watched movies on mental illness significantly related to “Responsibility” and “Fear” factors (P<0.05).

Programs designed to improve the health workers mental health literacy, and increased positive professional contacts with mentally ill persons on treatment, would further enhance their perceived positive attributions towards them.