New Insights Towards Developing Recommender Systems

Abstract

Promoting recommender systems in real-world applications requires deep investigations with emphasis on their next generation. This survey offers a comprehensive and systematic review on recommender system development life cycles to enlighten researchers and practitioners. The paper conducts statistical research on published recommender systems indexed by Web of Science to get an overview of the state of the art. Based on the reviewed findings, we introduce taxonomies driven by the following five phases: initiation (architecture and data acquisition techniques), design (design types and techniques), development (implementation methods and algorithms), evaluation (metrics and measurement techniques) and application (domains of applications). A layered framework of recommender systems containing market strategy, data, recommender core, interaction, security and evaluation is proposed. Based on the framework, the existing advanced humanized techniques emerged from computational intelligence and some inspiring insights from computational economics and machine learning are provided for researchers to expand the novel aspects of recommender systems.

Holistic User Context-Aware Recommender Algorithm

“…Using the concept of entropy and the linked items in the graph, the proposed algorithm was able to find recommendations that were both novel and relevant. e strategy of computing novel recommendations seems to be satisfactory from the status quo, but the argument still exists that serendipity is even more beneficial than novelty because it creates new preferences to the user thereby driving a user to make decisions in favor of the RS [7,19,25,26] argues that new methods of extracting implicit information about users from their daily activities can be used to realize novel and serendipitous recommendations. Alternative methods can be explored to rank and recommend items to users by considering several criteria [13,27,28,29], and that is the path taken by this article.…”

GRA-PIN: A Graphical and PIN-Based Hybrid Authentication Approach for Smart Devices

“…Usability is the relationship between the users and system that determines the quality of interactivity, which enables the ease of use to achieve defined goals with efficacy and satisfaction in the context in which a system or method is employed [ 31 ]. Usability can be decomposed into measurements and sub-measures such as effectiveness, learnability, task completion time, and satisfaction, among others [ 32 ].…”

Content and Other Resources Recommendations for Individuals with Intellectual Disability: A Review

“…Our key takeaway from reviewing the relevant literature is that approaches targeted to the general population do not match the needs of individuals with ID (from information acquisition to combating cold start problems, even on the design principles of the graphical user interface). The most prominent future directions for recommender systems for the general population concern scalability issues, i.e., using distributed and elastic platforms [140] (a direction that seems irrelevant when it comes to recommendation for individuals with ID, since such systems deal with a relatively small community, as identified in Section 2.2) and modeling the user using machine learning techniques [141]. Additionally, large corporations consider recommendation as a marketing opportunity, i.e., to gather more personal data in order to increase customer satisfaction and ultimately spending, whereas, when dealing with individuals with ID, the primary focus should include privacy-ensuring techniques (e.g., taking care of security vulnerabilities in decentralized environments) and how the social and ethical concerns associated with dealing with a vulnerable population can be safeguarded.…”