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Recommender systems provide personalized information access to users of Internet services from social networks to e-commerce to media and entertainment. As is appropriate for research in a field with a focus on personalization, academic studies of recommender systems have largely concentrated on optimizing for user experience when designing, implementing and evaluating their algorithms and systems. However, this concentration on the user has meant that the field has lacked a systematic exploration of other aspects of recommender system outcomes. A user-centric approach limits the ability to incorporate system objectives, such as fairness, balance, and profitability, and obscures concerns that might come from other stakeholders, such as the providers or sellers of items being recommended. Multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article outlines the multistakeholder perspective on recommendation, highlighting example research areas and discussing important issues, open questions, and prospective research directions.

We propose and compare combinations of several methods for classifying transportation activity data from smartphone GPS and accelerometer sensors. We have two main objectives. First, we aim to classify our data as accurately as possible. Second, we aim to reduce the dimensionality of the data as much as possible in order to reduce the computational burden of the classification. We combine dimension reduction and classification algorithms and compare them with a metric that balances accuracy and dimensionality. In doing so, we develop a classification algorithm that accurately classifies five different modes of transportation (i.e., walking, biking, car, bus and rail) while being computationally simple enough to run on a typical smartphone. Further, we use data that required no behavioral changes from the smartphone users to collect. Our best classification model uses the random forest algorithm to achieve 96.8% accuracy.

With the growth and prevalence of social media platforms, many companies have been using them to engage with customers and encourage user-generated content about their products and services. In this paper, we analyze user-generated posts from the Facebook business pages of multiple companies across several industries to understand what users post on Facebook business pages and how post valence and content characteristics affect engagement, measured as the number of likes and comments received by a post. Our analysis demonstrates that negative posts are significantly more prevalent than positive posts, and negative posts also tend to attract more likes and more comments than positive posts. Importantly, engagement depends not only on the valence of a post but also on the specific post content. We observe three types of customer complaints respectively related to product and service quality, money issues, and corporate social responsibility issues. We show that social complaints receive more likes, but fewer comments, than quality or money complaints. Our findings reveal the practical challenges of managing Facebook business pages as a new channel of interacting with customers, and they highlight the need to explore effective response strategies to manage customer complaints and other service requests on social media. With the growth and prevalence of social media platforms, many companies have been using them to engage with customers and encourage user-generated content (UGC) about their products and services. However, there has not been much research on the characteristics of UGC on these platforms and, correspondingly, their impact on customer engagement. In this paper, we analyze user-generated posts from Facebook business pages of multiple companies to understand what users post on Facebook business pages and how post valence and content characteristics affect engagement, measured as the number of likes and comments received by a post. We control for a variety of factors, including post linguistic features, poster characteristics, and post context heterogeneity. Our analysis demonstrates that for user-generated posts on Facebook business pages, negative posts are significantly more prevalent than positive posts, which contrasts with the J-shaped valence distribution of online consumer reviews. We also show that engagement depends not only on the valence of a post but also on the specific ways in which a post is positive or negative. We observe three types of customer complaints, respectively, related to product and service quality, money issues, and social and environmental issues. Our analyses show that social complaints receive more likes, but fewer comments, than quality or money complaints. Such nuances can only be uncovered by analyzing the actual post content, going beyond the valence of the posts. Furthermore, we theoretically discuss and empirically demonstrate that liking and commenting are engagement behaviors with different antecedents. For example, positive posts tend to attract more likes yet fewer comments than neutral posts. Overall, our research shows that user-generated posts on Facebook business pages represent a distinctive form of UGC that is conceptually different from online consumer reviews. Our work advances the knowledge on UGC and has practical implications for firms’ social media marketing strategy.

We develop a general-purpose agent-based simulation and modeling approach to analyze how user–recommender interactions affect recommender systems in the long run. Our explorations show that, over time, user–recommender interactions consistently lead to the longitudinal performance paradox of recommender systems. In particular, users’ reliance on recommendations, while helping users discover relevant items, actually hurts the future diversity of items that are recommended and consumed as well as slows down the system’s learning pace (i.e., the rate of predictive accuracy improvement). We also demonstrate unique benefits of certain hybrid consumption strategies—that is, that take advantage of both popularity- and personalization-based recommendations—in facilitating improvements in consumption relevance over time. Because users’ consumption strategies can significantly influence the longitudinal performance of recommender systems, it is important for designers to analyze the histories of a system’s recommendations and users’ choices to infer and understand users’ consumption strategies. This would enable the system to anticipate users’ consumption behavior and strategically adjust the system’s parameters according to its long-term performance objectives. We develop a general agent-based modeling and computational simulation approach to study the impact of various factors on the temporal dynamics of recommender systems’ performance. The proposed agent-based simulation approach allows for comprehensive analysis of longitudinal recommender systems performance under a variety of diverse conditions, which typically is not feasible with live real-world systems. We specifically focus on exploring the product consumption strategies and show that, over time, user–recommender interactions consistently lead to the longitudinal performance paradox of recommender systems. In particular, users’ reliance on the system’s recommendations to make item choices generally tends to make the recommender system less useful in the long run or, more specifically, negatively impacts the longitudinal dynamics of several important dimensions of recommendation performance. Furthermore, we explore the nuances of the performance paradox via additional explorations of longitudinal dynamics of recommender systems for a variety of user populations and consumption strategies, as well as personalized and nonpersonalized recommendation approaches. One interesting discovery from our exploration is that a certain hybrid consumption strategy—that is, where users rely on a combination of both personalized- and popularity-based recommendations, offers a unique ability to substantially improve consumption relevance over time. In other words, for such hybrid consumption settings, recommendation algorithms facilitate the general “quality-rises-to-the-top” phenomenon, which is not present in the pure popularity-based consumption. In addition to discussing a number of interesting performance patterns, the paper also analyzes and provides insights into the underlying factors that drive such patterns. Our findings have significant implications for the design and implementation of recommender systems.

We propose a new conceptual model for understanding technology evolution that highlights dynamic and highly interdependent relationships among multiple technologies. We argue that, instead of considering technologies in isolation, technology evolution is best viewed as a dynamic system or ecosystem that includes a variety of interrelated technologies. By considering the interdependent nature of technology evolution, we identify three roles that technologies play within a technology ecosystem. These roles are components, products and applications, and support and infrastructure. Technologies within an ecosystem interact through these roles and impact each others' evolution. We also classify types of interactions between technology roles, which we term paths of influence. We demonstrate the use of our proposed model through examples of wireless networking (Wi-Fi) technologies and a business mini-case on the digital music industry. [PUBLICATION ABSTRACT]

Prior research has shown that online recommendations have a significant influence on consumers’ preference ratings and economic behavior. Specifically, biases induced by observing personalized system recommendations can lead to distortions in users’ self-reported preference ratings after consumption of an item, thus contaminating the users’ subsequent inputs to the recommender system. This, in turn, provides the system with an inaccurate view of user preferences and opens up possibilities of rating manipulation. As recommender systems continue to become increasingly popular in today’s online environments, preventing or reducing such system-induced biases constitutes a highly important and practical research problem. In this paper, we address this problem via the analysis of different rating display designs for the purpose of proactively preventing biases before they occur (i.e., at rating collection time). We use randomized laboratory experimentation to test how the presentation format of personalized recommendations affects the biases generated in post-consumption preference ratings. We demonstrate that graphical rating display designs of recommender systems are more advantageous than numerical designs in reducing the biases, although none are able to remove biases completely. We also show that scale compatibility is a contributing mechanism operating to create these biases, although not the only one. Together, the results have practical implications for the design and implementation of recommender systems as well as theoretical implications for the study of recommendation biases.

The application of predictive data mining techniques in information systems research has grown in recent years, likely because of their effectiveness and scalability in extracting information from large amounts of data. A number of scholars have sought to combine data mining with traditional econometric analyses. Typically, data mining methods are first used to generate new variables (e.g., text sentiment), which are added into subsequent econometric models as independent regressors. However, because prediction is almost always imperfect, variables generated from the first-stage data mining models inevitably contain measurement error or misclassification. These errors, if ignored, can introduce systematic biases into the second-stage econometric estimations and threaten the validity of statistical inference. In this commentary, we examine the nature of this bias, both analytically and empirically, and show that it can be severe even when data mining models exhibit relatively high performance. We then show that this bias becomes increasingly difficult to anticipate as the functional form of the measurement error or the specification of the econometric model grows more complex. We review several methods for error correction and focus on two simulation-based methods, SIMEX and MC-SIMEX, which can be easily parameterized using standard performance metrics from data mining models, such as error variance or the confusion matrix, and can be applied under a wide range of econometric specifications. Finally, we demonstrate the effectiveness of SIMEX and MC-SIMEX by simulations and subsequent application of the methods to econometric estimations employing variables mined from three real-world data sets related to travel, social networking, and crowdfunding campaign websites. The online appendix is available at https://doi.org/10.1287/isre.2017.0727 .

Recommender systems are an integral part of the online retail environment. Prior research has focused largely on computational approaches to improving recommendation accuracy, and only recently researchers have started to study their behavioral implications and potential side effects. We used three controlled experiments, in the context of purchasing digital songs, to explore the willingness-to-pay judgments of individual consumers after being shown personalized recommendations. In Study 1, we found strong evidence that randomly assigned song recommendations affected participants’ willingness to pay, even when controlling for participants’ preferences and demographics. In Study 2, participants viewed actual system-generated recommendations that were intentionally perturbed (introducing recommendation error), and we observed similar effects. In Study 3, we showed that the influence of personalized recommendations on willingness-to-pay judgments was obtained even when preference uncertainty was reduced through immediate and mandatory song sampling prior to pricing. The results demonstrate the existence of important economic side effects of personalized recommender systems and inform our understanding of how system recommendations can influence our everyday preference judgments. The findings have significant implications for the design and application of recommender systems as well as for online retail practices. The online appendix is available at https://doi.org/10.1287/isre.2017.0703 .

Context‐aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in the recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context‐aware recommender systems.

Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve accuracy of predictions, although behavioral aspects of using recommender systems are often overlooked. In our studies, we explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender systems. We conducted three controlled laboratory experiments to explore the effects of system recommendations on preferences. Studies 1 and 2 investigated user preferences for television programs across a variety of conditions, which were surveyed immediately following program viewing. Study 3 investigated the granularity of the observed effects within individual participants. Results provide strong evidence that the rating presented by a recommender system serves as an anchor for the consumer's constructed preference. Viewers' preference ratings are malleable and can be significantly influenced by the recommendation received. The effect is sensitive to the perceived reliability of a recommender system and, thus, not a purely numerical or priming-based effect. Finally, the effect of anchoring is continuous and linear, operating over a range of perturbations of the system. These general findings have a number of important implications (e.g., on recommender systems performance metrics and design, preference bias, potential strategic behavior, and trust), which are discussed.