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In the domain of tourism and cultural creative product recommendations, the design and implementation of personalized recommendation systems are crucial for enhancing user satisfaction and engagement. Although traditional recommendation systems, such as Collaborative Filtering techniques, have been widely applied, they still face challenges in dealing with sparse data and achieving highly personalized recommendations. To solve this problem, a collaborative filtering recommendation method combining user profiles and multi-task knowledge acquisition is proposed in this study. We first construct consumer profiles with the aid of studying users behavioral facts and demographic statistics, after which layout a multi-task learning framework that improves the accuracy and personalization degree of tips through getting to know common functions of various however associated recommendation duties through shared representations. We evaluated our approach on numerous actual-international datasets, and the experimental outcomes display sizable improvements over conventional collaborative filtering strategies and other existing recommendation systems in key overall performance signs along with accuracy, recall, and F1-score. These results indicate that the collaborative filtering advice approach is more advantageous via the mixing of person profiles and multi-venture learning holds sizable software potential inside the subject of tourism metropolis cultural and innovative product recommendations.

Software reliability evaluation of complex systems is always a challenging task with conventional methods comprising both functional as well as nonfunctional aspects of real-world applications. Prevailing model frameworks moreover apply a nonfunctional approach (black-box model) that is modeled on defect data or through a functional approach (white-box model) that uses component or state-based interactions. Also, other challenges involve integrating both approaches, and validating user profiles of software operation. Further, reliability assessment is one among the most important and desirable qualities of service requirements of software systems, particularly in monitoring critical business transactions. Here, we propose a model framework to evaluate the overall reliability estimation involving both functional and nonfunctional model analyses using: (a) white-box assessment based on intercomponent analysis via component-based Cheung’s model and user profile validations with one of the identified deep learning techniques and (b) black-box modeling evaluation via generalized stochastic Petri nets based on orthogonal defect classification. A newly introduced deep learning model using white-box analysis is validated with pertinent usage profiles to establish a new trend in artificial neural networks and as well with software reliability estimation. Additionally, we introduce and present a quantitative technique — analytical hierarchy process — to integrate reliability assessment and provide weights to the white-box and as well for black-box approaches to quantify overall reliability estimation. The proposed framework is illustrated with an application case study.

We present a technique for simultaneously mining Web navigation patterns and maximally frequent context-sensitive itemsets (URL associations) from the historic user access data stored in Web server logs. A new hierarchical clustering technique that exploits the symbiosis between clusters in feature space and genetic biological niches in nature, called Hierarchical Unsupervised Niche Clustering (H-UNC) is presented. We use H-UNC as part of a complete system of knowledge discovery in Web usage data. Our approach does not necessitate fixing the number of clusters in advance, is insensitive to initialization, can handle noisy data, general non-differentiable similarity measures, and automatically provides profiles at multiple resolution levels. Our experiments show that our algorithm is not only capable of extracting meaningful user profiles on real Web sites, but also discovers associations between distinct URL pages on a site, with no additional cost. Unlike content based association methods, our approach discovers associations between different Web pages based only on the user access patterns and not on the page content. Also, unlike traditional context-blind association discovery methods, H-UNC discovers context-sensitive associations which are only meaningful within a limited context/user profile.

Currently, guiding students to reach the learning goals through problem-solving approach is quite primitive in most cyber-courseware systems on the Internet. In particular, these systems do not provide integrated learning-domain solutions for responding student enquiries based on individual student's background and interests. In order to offer student-specific problem-solving tutorship, dynamic generation of case-based solutions is a positive step towards this objective, which can accommodate individual student's needs accurately at anytime anywhere. In this paper, we present architecture and algorithms for developing an agent-based tutoring system called Coached Problem Solver (CPS) which, among other features, supports dynamic generation of student-oriented solutions on the Web. The facilities and capabilities of user profiles, rules, schema, XML and SMIL are incorporated and utilized by CPS so as to provide a better support to the interactive learning environment in which student-specific, content-based learning styles can be adequately entertained. A prototype system of CPS manifesting these algorithms has been built in Java, using IE on the PC platform.

User profiles were designed through a qualitative approach to a case study of a public library, starting from an analysis of space social relations, of how the building's design shaped reading practices and of how space was actually being used.