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Any business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective is to draw trustworthy conclusion, which results in brand building, and establishing a reliable relation with customers and undeniably to grow the business.

An experimental quantitative research method was conducted in which the ML model was evaluated with diversified performance metrics and five RE algorithms by combining offline evaluation on historical and simulated movie data set, and the online evaluation on business-alike near-real-time data set to uncover the best-fitting RE.

The rule-based automated evaluation of RE has changed the testing landscape, with the removal of longer duration of manual testing and not being comprehensive. It leads to minimal manual effort with high-quality results and can possibly bring a new revolution in the testing practice to start a service line “Machine Learning Testing as a service” (MLTaaS) and the possibility of integrating with DevOps that can specifically help agile team to ship a fail-safe RE evaluation product targeting SaaS (software as a service) or cloud deployment.

A small data set was considered for A/B phase study and was captured for ten movies from three theaters operating in a single location in India, and simulation phase study was captured for two movies from three theaters operating from the same location in India. The research was limited to Bollywood and Ollywood movies for A/B phase, and Ollywood movies for simulation phase.

The best-fitting RE facilitates the business to make personalized recommendations, long-term customer loyalty forecasting, predicting the company's future performance, introducing customers to new products/services and shaping customer's future preferences and behaviors.

The proposed rule-based ML approach named “2-stage locking evaluation” is self-learned, automated by design and largely produces time-bound conclusive result and improved decision-making process. It is the first of a kind to examine the business domain and task of interest. In each stage of the evaluation, low-performer REs are excluded which leads to time-optimized and cost-optimized solution. Additionally, the combination of offline and online evaluation methods offer benefits, such as improved quality with self-learning algorithm, faster time to decision-making by significantly reducing manual efforts with end-to-end test coverage, cognitive aiding for early feedback and unattended evaluation and traceability by identifying the missing test metrics coverage.

Recommender agents are used to make recommendations of interesting items in a wide variety of application domains, such as web page recommendation, music, e‐commerce, movie recommendation, tourism, restaurant recommendation, among others. Despite the various and different domains in which recommender agents are used and the variety of approaches they use to represent user interests and make recommendations, there is some functionality that is common to all of them, such as user model management and recommendation of interesting items. This paper aims at generalizing these common behaviors into a framework that enables developers to reuse recommender agents' main characteristics in their own developments.

This work presents a framework for recommendation that provides the control structures, the data structures and a set of algorithms and metrics for different recommendation methods. The proposed framework acts as the base design for recommender agents or applications that want to add the already modeled and implemented capabilities to their own functionality. In contrast with other proposals, this framework is designed to enable the integration of diverse user models, such as demographic, content‐based and item‐based. In addition to the different implementations provided for these components, new algorithms and user model representations can be easily added to the proposed approach. Thus, personal agents originally designed to assist a single user can reuse the behavior implemented in the framework to expand their recommendation strategies.

The paper describes three different recommender agents built by materializing the proposed framework: a movie recommender agent, a tourism recommender agent, and a web page recommender agent. Each agent uses a different recommendation approach. PersonalSearcher, an agent originally designed to suggest interesting web pages to a user, was extended to collaboratively assist a group of users using content‐based algorithms. MovieRecommender recommends interesting movies using an item‐based approach and Traveller suggests holiday packages using demographic user models. Findings encountered during the development of these agents and their empirical evaluation are described here.

The advantages of the proposed framework are twofold. On the one hand, the functionality provided by the framework enables the development of recommender agents without the need for implementing its whole set of capabilities from scratch. The main processes and data structures of recommender agents are already implemented. On the other hand, already existing agents can be enhanced by incorporating the functionality provided by the recommendation framework in order to act collaboratively.

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.

Customer engagement (customers’ behavioral manifestations going beyond customer-firm purchase transactions) has been regarded as strategic imperatives for generating enhanced corporate performance. The plethora of new media has provided customers with different options to interact with firms and other customers. However, the primacy of value-laden interactive customer relationships and value co-creation raises challenges for firms and customers, especially in the context of broader business ecosystems such as brand partnership for extending value co-creation. This study aims to explore how customer engagement with well-designed choreograph of various new media’s channels can increase the value co-creation extent in the context of broader business ecosystems, resulting in higher levels service offerings, experiences and innovation.

This exploratory study presents a new framework of customer engagement that holistically integrates the elements of multiple new media and broader business ecosystem, stimulating a virtuous circle of realizing customer engagement toward superior results or innovations. The framework considers new media’s different information service and technologies (e.g. search engine, social recommender, social media) that can be properly choreographed to achieve a virtuous customer engagement circle.

This paper uses an exemplar framework's instantiation – an information technology enabled engagement platform (called iEngagement) – that can demonstrate how to empower the central companies together with their eco-stakeholders to holistically perform customer engagement utilizing new media toward fruitful customer engagement.

This exploratory study is among the first that addresses the theory and practice of customer engagement within multiple new media and broader business ecosystem. This paper presents a customer engagement framework and an exemplified engagement platform that holistically integrate the elements of multiple new media and broader business ecosystem, for stimulating a virtuous circle of realizing customer engagement toward superior results or innovations.

A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems.

The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system.

The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations.

The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

The purpose of this paper is to present the results of a comparison among three different approaches for recommending learning objects (LO) inside a repository. The comparison focuses not only on prediction errors but also on the coverage of each tested configuration.

The authors compared the offline evaluation by using pure collaborative filtering (CF) algorithms with two other different combinations of pre-processed data. The first approach for pre-processing data consisted of clustering users according to their disciplines resemblance, while the second approach consisted of clustering LO according to their textual similarity regarding title and description. The three methods were compared with respect to the mean average error between predicted values and real values. Moreover, we evaluated the impact of the number of clusters and neighborhood size on the user-coverage.

Clustering LO has improved the prediction error measure with a small loss on user-coverage when compared to the pure CF approach. On the other hand, the approach of clustering users failed in both the error and in user-space coverage. It also became clear that the neighborhood size is the most relevant parameter to determine how large the coverage will be.

The methods proposed here were not yet evaluated in a real-world scenario, with real users opinions about the recommendations and their respective learning goals. Future work is still required to evaluate users opinions.

This research provides evidence toward new recommendation methods directed toward LO repositories.

This study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.

Thematic interviews were conducted with 20 Finnish young people aged 15–16 years. The material was analysed using qualitative content analysis, with a focus on everyday information practices involving online platforms.

The key finding of the study is that the current affordances of algorithmic recommendations enable users to engage in more passive practices instead of active search and evaluation practices. Two major themes emerged from the analysis: enabling not searching, inviting high trust, which highlights the how the affordances of algorithmic recommendations enable the delegation of search to a recommender system and, at the same time, invite trust in the system, and constraining finding, discouraging diversity, which focuses on the constraining degree of affordances and breakdowns associated with algorithmic recommendations.

This study contributes new knowledge regarding the ways in which algorithmic recommendations shape the information practices in young people's everyday lives specifically addressing the constraining nature of affordances.

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.

The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback.

CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is further enhanced using case-based reasoning in retrieving the top-5 results. A search engine prototype was developed using Text REtrieval Conference as the document collection, and results were evaluated at three levels (i.e. top-5, 10 and 15). A user evaluation involving 28 students was administered, focussing on 20 queries.

Both Mean Average Precision and Normalized Discounted Cumulative Gain results indicate CoRRe to have the highest retrieval precisions at all the three levels compared to the other feedback models. Furthermore, independent t-tests showed the precision differences to be significant. Rating was found to be the most popular technique among the participants, producing the best precision compared to referral and comments.

The findings suggest that search retrieval relevance can be significantly improved when users’ explicit feedback are integrated, therefore web-based systems should find ways to manipulate users’ feedback to provide better recommendations or search results to the users.

The study is novel in the sense that users’ comment, rating and referral were taken into consideration to improve their overall search experience.

Collaborative filtering (CF), one of the most popular recommendation techniques, is based on the principle of word-of-mouth communication between other like-minded users. The process of identifying these like-minded or similar users remains crucial for a CF framework. Conventionally, a neighbor is the one among the similar users who has rated the item under consideration. To select neighbors by the existing practices, their similarity deteriorates as many similar users might not have rated the item under consideration. This paper aims to address the drawback in the existing CF method where “not-so-similar” or “weak” neighbors are selected.

The new approach proposed here selects neighbors only on the basis of highest similarity coefficient, irrespective of rating the item under consideration. Further, to predict missing ratings by some neighbors for the item under consideration, ordinal logistic regression based on item–item similarity is used here.

Experiments using the MovieLens (ml-100) data set prove the efficacy of the proposed approach on different performance evaluation metrics such as accuracy and classification metrics. Apart from higher prediction quality, coverage values are also at par with the literature.

This new approach gets its motivation from the principle of the CF method to rely on the opinion of the closest neighbors, which seems more meaningful than trusting “not-so-similar” or “weak” neighbors. The static nature of the neighborhood addresses the scalability issue of CF. Use of ordinal logistic regression as a prediction technique addresses the statistical inappropriateness of other linear models to make predictions for ordinal scale ratings data.