A rule-based automated machine learning approach in the evaluation of recommender engine

Abstract

PurposeAny 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.Design/methodology/approachAn 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.FindingsThe 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.Research limitations/implicationsA 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.Practical implicationsThe 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.Originality/valueThe 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.

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

“…A test instance yielded 84% accuracy of the recommendation. Behera et al (2020) developed a rule based automated ML approach that also targets the same approach but on a different problem domain. Their recommender engine targets company performance forecasting when introducing new products or services.…”

Monetization of customer futures through machine learning and artificial intelligence based persuasive technologies

“…In the present-day context, Gen Z customer spent a lot of time on internet across different domains like email services and YouTube; social networking sites like Facebook; share text messaging like Twitter; photograph on Instagram or Pinterest; and others (Boczkowski et al, 2018;Tuten, 2020). Thus, marketing managers learned to comprehend a significant Systems that blend software utilities and procedures to offer customers suggestions on items of their specific interest This process helps the customers towards better decision-making RE technology-entailed usage of analytical logic to determine the probability that a customer would be interested to purchase a particular product or service (Behera et al, 2020;Kumar et al, 2005;Hossain et Multi-Armed Bandit Multi-Armed Bandit algorithm was devised on multi-armed bandit problems; in this, each arm represented an item of interest Multi-Armed Bandit algorithm chooses an arm to draw on the available user information by studying an item to be recommended When the item matched up with the user performance, which was indicative of the user performance, the desired action-based reward was obtained The reward was subsequently fed back to the system so as to further optimize the future initiatives (Zeng et al, 2016;Goswami et al, 2019) Monetization of customer futures manner and amount better regarding customer behaviour (like the needs, felt requirements, unexpressed and unfelt demands) (Appel et al, 2020;Steinhoff et al, 2019). Even the ecommerce website and m-commerce apps operating 24/7 were able to monitor Gen Z customers' actions, orientations, disliking, preferences and other behavioural characteristics (Quesenberry, 2020;Ashman et al, 2015).…”