A hybrid strategy for privacy-preserving recommendations for mobile shopping

To calculate recommendations, recommender systems col-lect and store huge amounts of users ’ personal data such as preferences, interaction behavior, or demographic infor-mation. If these data are used for other purposes or get into the wrong hands, the privacy of the users can be com-promised. Thus, service providers are confronted with the challenge of o↵ering accurate recommendations without the risk of dissemination of sensitive information. This paper presents a hybrid strategy combining collaborative filtering and content-based techniques for mobile shopping with the primary aim of preserving the customer’s privacy. Detailed information about the customer, such as the shopping his-tory, is securely stored on the customer’s smartphone and locally processed by a content-based recommender. Data of individual shopping sessions, which are sent to the store backend for product association and comparison with simi-lar customers, are unlinkable and anonymous. No uniquely identifying information of the customer is revealed, making it impossible to associate successive shopping sessions at the store backend. Optionally, the customer can disclose demo-graphic data and a rudimentary explicit profile for further personalization

Personalizing online reviews for better customer decision making

Online consumer reviews have become an important source of information for understanding markets and customer preferences. When making purchase decisions, customers increasingly rely on user-generated online reviews; some even consider the information in online reviews more credible and trustworthy than information provided by vendors. Many studies have revealed that online reviews influence demand and sales. Others have shown the possibility of identifying customer interest in product attributes. However, little work has been done to address customer and review diversity in the process of examining reviews. This research intends to answer the research question: how can we solve the problem of customer and review diversity in the context of online reviews to recommend useful reviews based on customer preferences and improve product recommendation? Our approach to the question is through personalization. Similar to other personalization research, we use an attribute-based model to represent products and customer preferences. Unlike existing personalization research that uses a set of pre-defined product attributes, we explore the possibility of a data-driven approach for identifying more comprehensive product attributes from online reviews to model products and customer preferences. Specifically, we introduce a new topic model for product attribute identification and sentiment analysis. By differentiating word co-occurrences at the sentence level from at the document level, the model better identifies interpretable topics. The use of an inference network with shared structure enables the model to predict product attribute ratings accurately. Based on this topic model, we develop attribute-based representations of products, reviews and customer preferences and use them to construct the personalization of online reviews. We examine personalization from the lens of consumer search theory and human information processing theory and test the hypotheses with an experiment. The personalization of online reviews can 1) recommend products matching customer's preferences; 2) improve custom's intention towards recommended products; 3) best distinguish recommended products from products that do not match customer's preferences; and 4) reduce decision effort

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4

Understanding Consumer Preferences for Explanations Generated by XAI Algorithms

Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there has not yet been sufficient consideration of customers' preferences for various types and formats of explanations. We discuss theoretically and study empirically people's preferences for explanations of algorithmic decisions. We focus on three main attributes that describe automatically-generated explanations from existing XAI algorithms (format, complexity, and specificity), and capture differences across contexts (online targeted advertising vs. loan applications) as well as heterogeneity in users' cognitive styles. Despite their popularity among academics, we find that counterfactual explanations are not popular among users, unless they follow a negative outcome (e.g., loan application was denied). We also find that users are willing to tolerate some complexity in explanations. Finally, our results suggest that preferences for specific (vs. more abstract) explanations are related to the level at which the decision is construed by the user, and to the deliberateness of the user's cognitive style.Comment: 18 pages, 1 appendix, 3 figures, 4 table

Evaluating product search and recommender systems for E-commerce environments

Online systems that help users select the most preferential item from a large electronic catalog are known as product search and recommender systems. Evaluation of various proposed technologies is essential for further development in this area. This paper describes the design and implementation of two user studies in which a particular product search tool, known as example critiquing, was evaluated against a chosen baseline model. The results confirm that example critiquing significantly reduces users' task time and error rate while increasing decision accuracy. Additionally, the results of the second user study show that a particular implementation of example critiquing also made users more confident about their choices. The main contribution is that through these two user studies, an evaluation framework of three criteria was successfully identified, which can be used for evaluating general product search and recommender systems in E-commerce environments. These two experiments and the actual procedures also shed light on some of the most important issues which need to be considered for evaluating such tools, such as the preparation of materials for evaluation, user task design, the context of evaluation, the criteria, the measures and the methodology of result analyse