Long-term user engagement in recommender systems: a review

2004

Abstract. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect p...

Computing Research Repository, 2002

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented viewpoint toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues--such as evaluation, targeting, and privacy and trust--which we also briefly address.

Journal of the American Society for Information Science and Technology, 2008

The purpose of this article is to critically deconstruct the term engagement as it applies to peoples' experiences with technology. Through an extensive, critical multidisciplinary literature review and exploratory study of users of Web searching, online shopping, Webcasting, and gaming applications, we conceptually and operationally defined engagement. Building on past research, we conducted semistructured interviews with the users of four applications to explore their perception of being engaged with the technology. Results indicate that engagement is a process comprised of four distinct stages: point of engagement, period of sustained engagement, disengagement, and reengagement. Furthermore, the process is characterized by attributes of engagement that pertain to the user, the system, and user-system interaction. We also found evidence of the factors that contribute to nonengagement. Emerging from this research is a definition of engagement-a term not defined consistently in past work-as a quality of user experience characterized by attributes of challenge, positive affect, endurability, aesthetic and sensory appeal, attention, feedback, variety/novelty, interactivity, and perceived user control. This exploratory work provides the foundation for future work to test the conceptual model in various application areas, and to develop methods to measure engaging user experiences.

Proceedings of the American Society for Information Science and Technology, 2008

Increased emphasis on user experiences with technology demonstrates that systems must be not only usable, but engaging. Engagement, defined as a quality of user experience, is a multidimensional construct characterized by aesthetic appeal, novelty, perceived challenge, feedback and control, attention, motivation, and affect. To measure engagement, we developed a multidimensional instrument and surveyed 440 online shoppers to assess its reliability and construct validity. Results of exploratory factor analysis showed that engagement is comprised of six distinct factors: perceived usability, aesthetics, focused attention, involvement, novelty, and endurability.

How do click rates vary between research paper recommendations that were previously shown to the same user and recommendations shown for the very first time? To answer this question we analyzed 31,942 research paper recommendation given to 1,155 students and researchers. Results indicate that recommendations should only be displayed once and click-through rates for 'fresh', i.e. previously unknown, recommendations are twice as high as for already known recommendations. However, results also show that some users are 'oblivious'. It frequently happened that users clicked on recommendations they already knew. In one case the same recommendation was shown six times to the same user and the user clicked on it each time again. Overall, around 50% of clicks on reshown recommendations were such 'oblivious-clicks'.

2019

Recommender systems have become a mainstay of modern internet applications. They help users identify products to purchase on Amazon, movies to watch on Netflix and songs to enjoy on Pandora. Indeed, they have become so commonplace that users, through years of interactions with these systems, have developed an inherent understanding of how recommender systems function, what their objectives are, and how the user might manipulate them. We describe this understanding as the Theory of the Recommender. In this pilot study, we design and administer a survey to 25 users familiar with recommender systems. Our detailed analysis of their responses demonstrates that they possess an awareness of how recommender systems profile the user, build representations for items, and ultimately construct recommendations. The success of this pilot study provides support for a larger user study and the development of a grounded theory to describe the user’s cognitive model of how recommender systems function.

AI Magazine

Recommender systems have been part of the Internet for almost two decades. Dozens of vendors have built recommendation technologies and taken them to market in two waves, roughly aligning with the web 1.0 and 2.0 revolutions. Today recommender systems are found in a multitude of online services. They have been developed using a variety of techniques and user interfaces. They have been nurtured with millions of users’ explicit and implicit preferences (most often with their permission). Frequently they provide relevant recommendations that increase the revenue or user engagement of the online services that operate them. However, when we evaluate the current generation of recommender systems from the point of view of the “recommendee,” we find that most recommender systems serve the goals of the business instead of their users’ interests. Thus we believe that the big promise of recommender systems has yet to be fulfilled. We foresee a third wave of recommender systems that act directl...

dcs.gla.ac.uk

User engagement is a key concept in designing user-centred web applications. It refers to the quality of the user experience that emphasises the positive aspects of the interaction, and in particular the phenomena associated with being captivated by technology. This definition is motivated by the observation that successful technologies are not just used, but they are engaged with. Numerous methods have been proposed in the literature to measure engagement, however, little has been done to validate and relate these measures and so provide a firm basis for assessing the quality of the user experience. Engagement is heavily influenced, for example, by the user interface and its associated process flow, the user's context, value system and incentives.

Conference on Recommender Systems, 2009

Social network sites rely on the contributions of t heir members to create a lively and enjoyable space. Recent rese arch has focused on using personalization and recommender technologies to encourage participation of existing members. In this work we present an early-intervention approach to encouragi ng participation and engagement, which makes recommendations to new users during their sign-up process. Our

2018

Although Galleries, Libraries, Archives, and Museums (GLAMs) increasingly encourage users to assist in the curation of online collections through open collaboration systems, measuring users’ engagement in these systems is a dynamic and complex challenge. We analyzed 18 user’s actions over 20 days according to the User Engagement Scale (UES) and based on Maximal Repeating Patterns (MRPs) and correlations between user interaction elements and dimensions of user engagement (focused attention, perceived usability, aesthetics, and reward). Our results show differences in usage tactics for users with high, medium, and low scores from UES, and monotonically increasing moderate correlations between perceived usability scores and game design elements. Additionally, we found that the longer the mean time interval between two consecutive user actions during a usage period lasted, the higher the UES score was. These results help to understand what influences user engagement, isolating the effec...