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Recommender agents are used to make recommendations of interesting items in a wide variety of application domains, such as web page recommendation, music, e‐commerce, movie recommendation, tourism, restaurant recommendation, among others. Despite the various and different domains in which recommender agents are used and the variety of approaches they use to represent user interests and make recommendations, there is some functionality that is common to all of them, such as user model management and recommendation of interesting items. This paper aims at generalizing these common behaviors into a framework that enables developers to reuse recommender agents' main characteristics in their own developments.

This work presents a framework for recommendation that provides the control structures, the data structures and a set of algorithms and metrics for different recommendation methods. The proposed framework acts as the base design for recommender agents or applications that want to add the already modeled and implemented capabilities to their own functionality. In contrast with other proposals, this framework is designed to enable the integration of diverse user models, such as demographic, content‐based and item‐based. In addition to the different implementations provided for these components, new algorithms and user model representations can be easily added to the proposed approach. Thus, personal agents originally designed to assist a single user can reuse the behavior implemented in the framework to expand their recommendation strategies.

The paper describes three different recommender agents built by materializing the proposed framework: a movie recommender agent, a tourism recommender agent, and a web page recommender agent. Each agent uses a different recommendation approach. PersonalSearcher, an agent originally designed to suggest interesting web pages to a user, was extended to collaboratively assist a group of users using content‐based algorithms. MovieRecommender recommends interesting movies using an item‐based approach and Traveller suggests holiday packages using demographic user models. Findings encountered during the development of these agents and their empirical evaluation are described here.

The advantages of the proposed framework are twofold. On the one hand, the functionality provided by the framework enables the development of recommender agents without the need for implementing its whole set of capabilities from scratch. The main processes and data structures of recommender agents are already implemented. On the other hand, already existing agents can be enhanced by incorporating the functionality provided by the recommendation framework in order to act collaboratively.

The purpose of this paper is to describe UWIRS (Ubiquitous Web Information Retrieval Solution), an agent‐based Web Information Retrieval (WIR) solution designed taking into account the unique features of the World Wide Web (WWW) and the limitations of existing WIR solutions for ubiquitous environments.

UWIRS can offer recommendation services by using the Multi‐Agent Vizier Recommendation Framework (Vizier). Vizier was designed under a generic approach and therefore can provide services to information retrieval applications so these may offer product recommendations that consider several adaptation/personalization dimensions (e.g. user dimension, context, among others).

Overall, the main challenge resides on: location, retrieval, integration and presentation of information from the WWW, quickly and accurately, to satisfy a user's singular information needs.

In UWIRS, agents cooperate in order to retrieve personalized information, considering user needs, goals, preferences and contextual features. UWIRS's agents are responsible for: interpreting user input and adding adaptation information by means of a query enrichment process; identifying and selecting the appropriate data sources taking into consideration the Profile Set (composed of User, Device and Information‐Provider Profiles); executing query routing and the information retrieval process; integrating and filtering the retrieved results; and lastly, coherent presentation of quality and relevant ubiquitous information (anytime, anywhere and anyhow) that satisfies the user's particular information needs and constraints associated to his/her access device.

The Internet has become a popular medium for information exchange and knowledge delivery. Several traditional social activities have moved to the Internet, such as distance learning, tele‐medical system and. traditional buying and selling activities. Online merchants must know what users want, so providing recommendation services is an important strategy. Analyzes users’ on‐line behavior and interests, and recommends to them new or potential products. The analysis mechanism is based on the correlation among customers, product items, and product features. An algorithm is developed to classify users into groups and the recommendation is based on the classification. The system can help merchants to make suitable business decisions and provide personalized information to the customers. A generic mobile agent framework for e‐commerce applications is proposed. The aforementioned collaborative computing architecture for the recommendation system is based on the framework.

The purpose of this paper is an exploratory study of customers’ “lived” experiences of commercial recommendation services to better understand customer expectations for personalization with recommendation agents. Recommendation agents programmed to “learn” customer preferences and make personalized recommendations of products and services are considered a useful tool for targeting customers individually. Some leading service firms have developed proprietary recommender systems in the hope that personalized recommendations could engage customers, increase satisfaction and sharpen their competitive edge. However, personalized recommendations do not always deliver customer satisfaction. More often, they lead to dissatisfaction, annoyance or irritation.

The critical incident technique is used to analyze customer satisfactory or dissatisfactory incidents collected from online group discussion participants and bloggers to develop a classification scheme.

A classification scheme with 15 categories is developed, each illustrated with satisfactory incidents and dissatisfactory incidents, defined in terms of an underlying customer expectation, typical instances of satisfaction and dissatisfaction and, when possible, conditions under which customers are likely to have such an expectation. Three pairs of themes emerged from the classification scheme. Six tentative research propositions were introduced.

Findings from this exploratory research should be regarded as preliminary. Besides, content validity of the categories and generalizability of the findings should be subject to future research.

Research findings have implications for identifying priorities in developing algorithms and for managing personalization more strategically.

This research explores response to personalization from a customer’s perspective.

The purpose of this paper is to evaluate how the intermediation of an online agent in the relationship between prospective clients and professional service providers affects individual purchasing processes and decisions, and satisfaction with the professional service provider once the commercial transaction is concluded.

Drawing on the integrated trust-technology acceptance model, modified to include two additional variables to take into account of the specificities of the context investigated – users’ perceived reduction of information asymmetry and satisfaction with the professional service provider – a research framework is developed and tested with a research design combining a decision tree procedure with structural equation modelling and multi-group analysis. Participants are 188 users of an Italian website which incorporates an online agent that refers notaries to prospective clients.

Decisions to purchase professional services brokered by online agents depend upon trust in the agent, and users’ perceptions of the agent’s ability to reduce information asymmetry, as well as its perceived usefulness. Online agents for professional services can be effective as well as efficient: users who bought the service from an agent-referred notary had higher levels of satisfaction with their professional service provider than users who purchased the service from a different notary.

This is the first empirical effort to investigate the effects of online agents in the specific context of professional service purchasing. The uniqueness of the research context permitted identification of a new type of online agent, the “double-sided online referral agent”.

In the context of a profound digital transformation, the need for social interactivity is becoming fundamental for consumers on e-commerce sites. It allows them to interact with the company in the same way as with salespeople in physical stores. Among the different existing virtual agents used by companies to offer online solid interaction, this study focuses on virtual recommendation agents (VRAs). The purpose of this paper is to investigate the effectiveness of VRA on consumers’ psychological states and online impulse buying.

An experimental website was designed for this study. After interacting with VRA, respondents had to take part in a survey. The questionnaire included measures of perception of the VRA, perceived enjoyment, online impulse buying and perceived risk. Structural equation modelling was used to test the research model.

The results confirm the positive influence of the VRA on perceived enjoyment, which is positively associated with online impulse buying. The effect of the VRA’s presence on perceived enjoyment is moderated by gender.

Only one product category was studied, for which the advice of VRAs is undoubtedly essential. However, this could also be valid for other products, such as technological products, where the consumer’s level of expertise may be low. Hence, the authors propose to extend this study to various products for a better generalization of the results.

This study provides practitioners with relevant findings on the efficiency of VRAs and offers them guidelines to design more interactive commercial websites with higher levels of social interactions. Such interactions should reduce perceived risks and make visitors more confident. This can encourage more traffic and sales, which implies growth in incomes and revenues.

Through this technology, VRAs can create more humanized links between consumers and companies.

Working on VRAs is original as they represent the technology that can replace salespeople. In addition, to the best of the authors’ knowledge, this research is the first to test the impact of VRA on online impulse buying. By examining the VRA’s set of fundamental capabilities, this study contributes to existing research on how companies should integrate digital technologies in their sales interactions with consumers, which to date has focused on other sales channels such as social media platforms.

This study aims to understand how travelers evaluate travel destination recommendations received from either artificial intelligence (AI) or human travel experts (TEs) based on the size of recommendation and their travel involvement.

This study used a 2 (agent type: AI vs TE) × 2 (size of recommendation: small vs large) × 2 (travel involvement: low vs high) between-subjects design.

When AI recommends destinations, less-involved travelers perceive the recommendations as more credible and trust the system when AI offers larger recommendations than smaller ones. Meanwhile, when TEs offer recommendations, travelers consider the recommendations as equally credible and similarly trust the system, regardless of the recommendation size and travel involvement.

This study sheds light on the design of human-centered AI travel destination recommendation services.

本研究旨在了解旅行者如何根据推荐的规模和他们的旅行参与度来评估从人工智能 (AI) 或人类旅行专家 (TE) 收到的旅行目的地推荐。

本研究使用 2（代理类型：AI 与 TE）×2（推荐数量：小与大）×2（旅行参与：低与高）受试者间设计。

当 AI 推荐目的地时, 参与度较低的旅行者认为推荐更可信, 并且当 AI 提供的建议比较小的建议大时信任系统。 同时, 当 TE 提供推荐时, 无论推荐数量大小和旅行参与度如何, 旅行者都认为这些推荐同样可信并且同样信任系统。

这项研究揭示了以人为本的人工智能旅游目的地推荐服务的设计。

This paper seeks to review current research on assistive consumer technologies (ACT 1.0) and to discuss a series of research challenges that need to be addressed before the field can move towards tools that are more effective and more readily adopted by consumers (ACT 2.0).

This is a conceptual paper. The perspective, commensurate with the current research and areas of expertise, is that of consumer researchers.

The paper argues that, while substantial advances have been made in the technical design of ACTs – and the algorithms that power recommendation systems, there are substantial barriers to wide‐scale consumer adoption of such tools that need to be addressed. In particular, future ACT designs will need to better integrate current research in human judgment and decision making to improve the ease with which such tools can be used.

From the perspective of consumer researchers, the paper highlights a set of key areas of enquiry that have the potential to substantially advance assistive consumer technology research.

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market.

This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords.

This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system.

Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

Taking into account the importance of e‐commerce and the current applications of AI techniques in this area, this research aims to adequate the design of a multi‐agent system for the provisioning of e‐services in u‐commerce environments. This proposal is centred on the methods of evaluation in a u‐e‐commerce environment.

The multi‐agent systems (MAS) approach is based on an MAS model developed for AmI that has been redesigned to support u‐commerce. The use of a recommendation system, previously developed by the research group, is suggested for this MAS. The methodological proposal centres on the evaluation of this type of system.

The evaluation of this type of system is the principal problem of current research. Therefore, this is the main contribution of the paper.

The different evaluation methods that are proposed, whether qualitative or quantitative, offer the possibility of measuring the added value that the context can give to the use of e‐services in different domains of application. Qualitative evaluation should consider the customer as a central piece in the system. In addition, quantitative methods should objectively evaluate the contribution of context to the application.

At present, there is no single method for evaluating the benefits of different u‐commerce systems, so a new method needs to be found based on these techniques.

The research proposes an MAS designed for u‐commerce domains, analyzes the capacity of trust management techniques in this environment, and proposes several evaluation methods to show the benefits of context information in the use of e‐services. Several real developments are described to show the different applications of MAS in u‐commerce and how evaluation is carried out.