Synthesizing aspect-driven recommendation explanations from reviews

National Research Foundation (NRF) Singapore under NRF Fellowship Programm

Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI

Explainable recommendation with comparative constraints on product aspects

National Research Foundation (NRF) Singapore under NRF Fellowship Programm

Algorithmic Transparency: Concepts, Antecedents, and Consequences – A Review and Research Framework

The widespread and growing use of algorithm-enabled technologies across many aspects of public and private life is increasingly sparking concerns about the lack of transparency regarding the inner workings of algorithms. This has led to calls for (more) algorithmic transparency (AT), which refers to the disclosure of information about algorithms to enable understanding, critical review, and adjustment. To set the stage for future research on AT, our study draws on previous work to provide a more nuanced conceptualization of AT, including the explicit distinction between AT as action and AT as perception. On this conceptual basis, we set forth to conduct a comprehensive and systematic review of the literature on AT antecedents and consequences. Subsequently, we develop an integrative framework to organize the existing literature and guide future work. Our framework consists of seven central relationships: (1) AT as action versus AT as perception; factors (2) triggering and (3) shaping AT as action; (4) factors shaping AT as perception; as well as AT as perception leading to (5) rational-cognitive and (6) affective-emotional responses, and to (7) (un-)intended behavioral effects. Building on the review insights, we identify and discuss notable research gaps and inconsistencies, along with resulting opportunities for future research

Best practice in undertaking and reporting health technology assessments : Working Group 4 report

[Executive Summary] The aim of Working Group 4 has been to develop and disseminate best practice in undertaking and reporting assessments, and to identify needs for methodologic development. Health technology assessment (HTA) is a multidisciplinary activity that systematically examines the technical performance, safety, clinical efficacy, and effectiveness, cost, costeffectiveness, organizational implications, social consequences, legal, and ethical considerations of the application of a health technology (18). HTA activity has been continuously increasing over the last few years. Numerous HTA agencies and other institutions (termed in this report “HTA doers”) across Europe are producing an important and growing amount of HTA information. The objectives of HTA vary considerably between HTA agencies and other actors, from a strictly political decision making–oriented approach regarding advice on market licensure, coverage in benefits catalogue, or investment planning to information directed to providers or to the public. Although there seems to be broad agreement on the general elements that belong to the HTA process, and although HTA doers in Europe use similar principles (41), this is often difficult to see because of differences in language and terminology. In addition, the reporting of the findings from the assessments differs considerably. This reduces comparability and makes it difficult for those undertaking HTA assessments to integrate previous findings from other HTA doers in a subsequent evaluation of the same technology. Transparent and clear reporting is an important step toward disseminating the findings of a HTA; thus, standards that ensure high quality reporting may contribute to a wider dissemination of results. The EUR-ASSESS methodologic subgroup already proposed a framework for conducting and reporting HTA (18), which served as the basis for the current working group. New developments in the last 5 years necessitate revisiting that framework and providing a solid structure for future updates. Giving due attention to these methodologic developments, this report describes the current “best practice” in both undertaking and reporting HTA and identifies the needs for methodologic development. It concludes with specific recommendations and tools for implementing them, e.g., by providing the structure for English-language scientific summary reports and a checklist to assess the methodologic and reporting quality of HTA reports

A SYSTEMATIC REVIEW OF COMPUTATIONAL METHODS IN AND RESEARCH TAXONOMY OF HOMOPHILY IN INFORMATION SYSTEMS

Homophily is both a principle for social group formation with like-minded people as well as a mechanism for social interactions. Recent years have seen a growing body of management research on homophily particularly on large-scale social media and digital platforms. However, the predominant traditional qualitative and quantitative methods employed face validity issues and/or are not well-suited for big social data. There are scant guidelines for applying computational methods to specific research domains concerning descriptive patterns, explanatory mechanisms, or predictive indicators of homophily. To fill this research gap, this paper offers a structured review of the emerging literature on computational social science approaches to homophily with a particular emphasis on their relevance, appropriateness, and importance to information systems research. We derive a research taxonomy for homophily and offer methodological reflections and recommendations to help inform future research

Extending the Foresight of Phillip Ein-Dor: Causal Knowledge Analytics

Phillip Ein-Dor advocated that electronic journals be more than a PDF of the established text model. He envisioned a transformation of scholarship. The need for such a transition has only grown since the first issue of JAIS in 2000 because the continuing growth and fragmentation of knowledge limits the generation of new knowledge. We propose drawing on analytics and AI to accelerate and transform scholarship, providing an appropriate tribute to a visionary IS leader

Factors Affecting the Scientific Impact of Literature Reviews: A Scientometric Study

Standalone literature reviews are fundamental in every scientific discipline. Their value is reflected by a profound scientific impact in terms of citations. Although previous empirical research has shown that this impact has a large variance, it is largely unknown which specific factors influence the impact of literature reviews. Against this background, the purpose of our study is to shed light on the driving factors that make a difference in the scientific impact of literature reviews. Our analysis of an exhaustive set of 214 IS literature reviews reveals that factors on the author level (e.g., expertise, collaboration, and conceptual feedback) and on the article level (e.g., methodological rigor) are significant and robust predictors of scientific impact over and above journal level factors. These insights enhance our understanding of what distinguishes highly cited literature reviews. In so doing, our study informs future guidelines on literature reviews and provides insights for prospective authors