Fabiana Vernero

Tag-based user modeling for social multi-device adaptive guides

This paper aims to demonstrate that the principles of adaptation and user modeling, especially social annotation, can be integrated fruitfully with those of the web 2.0 paradigm and thereby enhance in the domain of cultural heritage. We propose a framework for improving recommender systems through exploiting the users tagging activity. We maintain that web 2.0’s participative features can be exploited by adaptive web-based systems in order to enrich and extend the user model, improve social navigation and enrich information from a bottom-up perspective. Thus our approach stresses social annotation as a new and powerful kind of feedback and as a way to infer knowledge about users. The prototype implementation of our framework in the domain of cultural heritage is named iCITY. It is serving to demonstrate the validity of our approach and to highlight the benefits of this approach specifically for cultural heritage. iCITY is an adaptive, social, multi-device recommender guide that provides information about the cultural resources and events promoting the cultural heritage in the city of Torino. Our paper first describes this system and then discusses the results of a set of evaluations that were carried out at different stages of the systems development and aimed at validating the framework and implementation of this specific prototype. In particular, we carried out a heuristic evaluation and two sets of usability tests, aimed at checking the usability of the user interface, specifically of the adaptive behavior of the system. Moreover, we conducted evaluations aimed at investigating the role of tags in the definition of the user model and the impact of tags on the accuracy of recommendations. Our results are encouraging.

Choice Architecture for Human-Computer Interaction

People in human–computer interaction have learned a great deal abouthow to persuade and influence users of computing technology. Theyhave much less well-founded knowledge about how to help users choosefor themselves. Its time to correct this imbalance. A first step is toorganize the vast amount of relevant knowledge that has been builtup in psychology and related fields in terms of two comprehensive buteasy-to-remember models: The ASPECT model answers the question“How do people make choices?“ by describing six choice patterns thatchoosers apply alternately or in combination, based on Attributes, Socialinfluence, Policies, Experience, Consequences, and Trial and error.The ARCADE model answers the question “How can we help peoplemake better choices?“ by describing six general high-level strategies forsupporting choice: Access information and experience, Represent thechoice situation, Combine and compute, Advise about processing, Designthe domain, and Evaluate on behalf of the chooser. These strategiescan be implemented with straightforward interaction design, butfor each one there are also specifically relevant technologies. Combiningthese two models, we can understand virtually all existing and possibleapproaches to choice support as the application of one or more of theARCADE strategies to one or more of the ASPECT choice patterns.After introducing the idea of choice architecture for human–computerinteraction and the key ideas of the ASPECT and ARCADEmodels, we discuss each of the Aspect patterns in detail and show howthe high-level ARCADE strategies can be applied to it to yield specifictactics. We then apply the two models in the domains of online communitiesand privacy. Most of our examples concern choices about theuse of computing technology, but the models are equally applicable toeveryday choices made with the help of computing technology.

Interacting with social networks of intelligent things and people in the world of gastronomy

This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure. The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.

Towards a customization of rating scales in adaptive systems

In web-based adaptive systems, the same rating scales are usually provided to all users for expressing their preferences with respect to various items It emerged from a user experiment that we recently carried out that different users show different preferences with respect to the rating scales to use in the interface of adaptive systems, given the particular topic they are evaluating Starting from this finding, we propose to allow users to choose the kind of rating scale they prefer This approach raises various issues; the most important is that of how an adaptation algorithm can properly deal with values coming from heterogeneous rating scales We conducted an experiment to investigate how users rate the same object on different rating scales On the basis of our interpretation of these results, as an example of one possible solution approach, we propose a three-phase normalization process for mapping preferences expressed with different rating scales onto a unique system representation.

The evaluation of a social adaptive website for cultural events

In this paper, we present an evaluation of a social adaptive website in the domain of cultural events, iCITY DSA, which provides information about cultural resources and events that promote the cultural heritage in the city of Turin. Using this evaluation, our objective was to investigate the actual usage of a social adaptive website, in an effort to discover the real behavior of users, the unforeseen correlations among user actions and the consequent interactive behavior, the accuracy of both system and social recommendations and their impact on the users themselves, and the role of tagging in the user modeling process. The major contributions of the paper are manifold: insights into user interactions with social adaptive systems; guidelines for future designs; evaluation of the tagging activity and tag meanings in relation to the application domain and thus their impact on the representation of the user model; and a demonstration of how a combination and interplay of evaluation methodologies (e.g., quantitative and qualitative) can enhance our comprehension of evaluation data.

Supporting content discovery and organization in networks of contents and users

The design of approaches for supporting the user in the navigation of a variety of contents is an interesting area of research with many potential applications. In particular, interactive television (iTV) offers users the opportunity of accessing a huge amount of contents, ranging from general to specialized ones. As a consequence, the exploration of such contents must be supported in some way. In this paper, we present an innovative approach that integrates some recent methodologies and technologies developed in different areas of Artificial Intelligence and the Web: user-model-based adaptation, social networking, semantic annotation, and content sharing. We show how the integration of these technologies can provide interesting opportunities for a new approach to content navigation and discovery based on the possibility of exploring personalized networks of contents, users and concepts. Also, we focus on the specific goal of designing a platform for accessing iTV contents with the aim of providing the user with many alternative ways of exploring and discovering potentially interesting videos. After discussing the application, integrated in a project by Telecom Italia for a new paradigm of iTV, we preset the architecture we designed for integrating the methodologies discussed above.

Unveiling the link between logical fallacies and web persuasion

In the last decade Human-Computer Interaction (HCI) has started to focus attention on persuasive technologies having the goal of changing users behavior and attitudes according to a predefined direction. In this work, we hypothesize a strong connection between logical fallacies (forms of reasoning which are logically invalid but cognitively effective) and some common persuasion strategies adopted within web technologies. With the aim of empirically evaluating our hypothesis, we carried out a pilot study on a sample of 150 e-commerce websites.

Double-Sided Recommendations: A Novel Framework for Recommender Systems

Recommender systems actively provide users with suggestions of potentially relevant items. In this paper we introduce double-sided recommendations, i.e., recommendations consisting of an item and a group of people with whom such an item could be consumed. We identify four specific instances of the double-sided recommendation problem and propose a general method for solving each of them (social comparison-based, group-priority, item-priority and samepriority methods), thus defining a framework for generating double-sided recommendations. We present the experimental evaluation we carried out, focusing on the restaurant domain as a use case, with the twofold aim of 1) assessing user liking for double-sided recommendations and 2) comparing the four proposed methods, testing our hypothesis that their perceived usefulness varies according to the specific problem instance users are facing. Our results show that users appreciate double-sided recommendations and that all four methods -and, in particular, the group-priority one- can generate useful suggestions.

How scales influence user rating behaviour in recommender systems

ABSTRACT Many websites allow users to rate items and share their ratings with others, for social or personalisation purposes. In recommender systems in particular, personalised suggestions are generated by predicting ratings for items that users are unaware of, based on the ratings users provided for other items. Explicit user ratings are collected by means of graphical widgets referred to as ‘rating scales’. Each system or website normally uses a specific rating scale, in many cases differing from scales used by other systems in their granularity, visual metaphor, numbering or availability of a neutral position. While many works in the field of survey design reported on the effects of rating scales on user ratings, these, however, are normally regarded as neutral tools when it comes to recommender systems. In this paper, we challenge this view and provide new empirical information about the impact of rating scales on user ratings, presenting the results of three new studies carried out in different domains. Based on these results, we demonstrate that a static mathematical mapping is not the best method to compare ratings coming from scales with different features, and suggest when it is possible to use linear functions instead.

A Study on User Preferential Choices about Rating Scales

Websites usually offer the same rating scale for all users and all tasks, but users can have very different preferences. In this paper, the authors study rating scales from the point of view of preferential choices, investigating i if user preferences for rating scales depend on the object to evaluate, and ii if user preferences change after they have rated an object repeatedly, gaining a high level of experience with the evaluated object. The authors first defined a model of rating scales, identifying generic classes based on features like granularity and visual metaphor. Then, the authors had users choose between three scales, one for each class, for rating two objects with opposite features, first in a condition where users had a low level of experience, and then in a condition where their level of experience was high. Results showed that user choices depend on the evaluated objects, while their level of experience influences their overall preferences, but not their choices when they have to rate a specific object. The authors conclude with some insights and guidelines for designers of interactive systems.