Don’t Take it Personally: Resistance to Individually Targeted Recommendations with Anthropomorphic Recommender Systems

Recommender agents, artificially intelligent recommender systems that demonstrate anthropomorphic cues, are widely available online to provide consumers with individually tailored recommendations. Nevertheless, little is known about the effect of their anthropomorphic cues on users’ resistance to both the system and recommendations. Moreover, individually tailored recommendations require users to disclose information proactively or reactively for receiving customized or personalized recommendations, which can trigger users’ resistance to the platform and the recommendations. Accordingly, this study examined the extent to which recommender systems’ anthropomorphic cues and the type of recommendations provided (customized and personalized) influenced online users’ perceptions of control, trustworthiness, and the risk of using the platform. The study assessed how these perceptions, in turn, influence users’ adherence to the recommendations. An online experiment among online users (N = 266) with recommender agents and web recommender platforms that provided customized or personalized restaurant recommendations was conducted. The results of the experiment entail that when recommendations are customized, as compared to personalized, users are less likely to demonstrate resistance and are more likely to adhere to the recommendations provided. Furthermore, the study’s findings suggest that these effects are amplified for recommender agents, demonstrating anthropomorphic cues, in contrast to traditional systems as web recommender platforms.

Intelligent Recommender Systems in E-Commerce

Among thousands of alternatives, most of the time online customers cannot easily decide on which product to purchase or service to utilize. In order to assist online customers in their decision-making process, business owners have started to make their online platforms more intelligent by enhancing their platforms with intelligent recommender systems. Recommender systems, also known as recommender agents or intelligent agents, are intelligent software that provide easily accessible, personalized, highly relevant, and high-quality recommendations to customers in various online platforms. This chapter discusses different types of recommender systems and provides use case examples of recommender systems in various e-commerce platforms. This chapter shows how recommender systems make life easier for online customers in the constantly developing and growing internet environment. This chapter also discusses the challenges posed by recommender systems to online customers.

Map- or list-based recommender agents? Does the map metaphor fulfill its promise?

We present a spatialization of digital library content based on item similarity and an experiment which compares the performance of this spatialization relative to a simple list-based display. Items in the library are elementary school, middle school, and high school science and engineering learning resources. Spatialization and visualization are accomplished through two-dimensional interactive Sammon mapping of pairwise item similarities computed from the joint occurrence of word bigrams. The 65 science teachers participating in the experiment were asked to search the library for curricular items they would consider using as part of one or more teaching assignments. The results indicate that whereas the spatializations adequately capture the salient features of the library’s content and teachers actively use them, item retrieval rates, task-completion time, and perceived utility do not significantly differ from the semantically poorer but easier to comprehend and navigate list-based representations. These results put into question the usefulness of the rapidly increasing supply of information spatializations.

Modeling Knowledge and Reasoning in Conversational Recommendation Agents

This chapter introduces conversational recommender agents that facilitate user navigation through a product space, alternatively making concrete product suggestions and eliciting the user’s feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a feature’s value-space. For example, a user may request a cheaper product, thus critiquing the price feature. One of the most important objectives in a recommender agent is to discover, with minimal user feedback, which are the user’s product preferences. For this purpose, the chapter includes recent research on critiquing-based recommendation and a comparison between standard and recent proposals of recommendation based on critiquing.

How to Build up Recommender Agents, Step by Step

This chapter proposes a novel educational approach to agents, emphasizing the hand-on practical application of agents, the direct implementation of agency features without any strict methodology, boost the students’ excitement through competition while enhancing the necessary students’ cooperative skills through the development of auction market places. This chapter introduces, step by step, the agency features necessary to construct a recommender agent: user profiling and recommender systems, trust, aggregation-consensus, and negotiation-auctions. Following the aim of hands-on priority, other more advanced topics, such as e-Institutions, multi-agent architectures, and agent programming languages, are intentionally not covered in this chapter, though covered for more specialized courses. The contents of this chapter were developed for the “Tecnologia Agent (TA)” master course in the years 2005-2010 with post-graduate students at the University of Girona, who demonstrated high levels of achievement by grasping a way of building up agents, less concerned of methodologies and more focused on mastering the agent features necessary to build up agents that autonomously work on behalf of users.