Jhilmil Jain

Best practices in longitudinal research

This workshop will identify best practices for longitudinal research through an in-depth exploration of methods and metrics for collecting and analyzing user data over time. This is the fourth event in an ongoing effort by the organizers to enhance our current body of knowledge about longitudinal research.

Mobile In-store Personalized Services

The Mobile Shopping Assistant (MSA) is a mobile application platform to deliver real-time, in-store, and personalized services, such as personalized product offerings and in-store customer advisory support, to improve the shopping experiences of in-store customers. The service delivery network that powers the MSA involves retail stores and their business partners such as manufacturers. This paper presents the core technologies that we developed in this cross-organizational service network to support the MSA and its personalized services, with focus on service delivery, customer behavior understanding and information sharing. Our event-based techniques allow customers, stores and manufacturers to deliver and consume the services in a loosely coupled manner, thus solving a critical store-specific real-time engagement problem in a seamless way. Service response tracking enables the stores to construct a comprehensive view of a customer’s in-store shopping behavior. Finally, the cross-organizational authorization-based access control mechanism effectively enforces information sharing between the stores and their partners.

Color match: an imaging based mobile cosmetics advisory service

In this paper we describe an exploratory study of a mobile cosmetic advisory system that enables women to select appropriate colors of cosmetics. This system is intended for commercial use to address the problem of foundation color selection. Although women are primarily responsible for making most purchasing decisions in the US, we found very few studies to assess the adoption of retail related mobile services by women. Based on surveys, semi-structured interviews, and focus groups, we have identified a number of design factors that should be considered when designing mobile services for women consumers. The results of our study indicate that while usefulness is an important factor, other design aspects such as mobile vs. kiosk, installed vs. existing software, technical comfort vs. social comfort, social vs. individual, privacy and trust should also be accounted for.

Multimodal capture of consumer intent in retail

In this paper we present a prototype for capturing retail related consumer intent using multiple devices and in multimodal input formats such as text, audio, and still images. The prototype was used in a longitudinal user study to analyze the process that consumers go through in order to make purchasing decisions. Based on these findings, we recommend desirable features for information management systems specifically designed for the retail environment.

How to bring HCI research and practice closer together

This special interest group probes potential problems between HCI researchers and the practitioners who are consumers of research, to explore the extent of the problems and propose possible solutions. It will start with the results of the CHI 2010 workshop on the same topic, articulating factors that may render some of the research literature inaccessible or irrelevant to practitioners. When should HCI researchers be concerned about the relevance of their work to practitioners? How should practitioners communicate their needs for research? Participants will discuss these topics and others that both groups can use to help bridge the gap between research and practice in HCI.

Multimodal Shopping Lists

In this paper we present a prototype for creating shopping lists using multiple input devices such as desktop, smart phones, landline or cell phones and in multimodal formats such as structured text, audio, still images, video, unstructured text and annotated media. The prototype was used by 10 participants in a two week longitudinal study. The goal was to analyze the process that users go through in order to create and manage shopping related projects. Based on these findings, we recommend desirable features for personal information management systems specifically designed for managing collaborative shopping lists.

Researcher-practitioner interaction

This workshop explores whether problems exist between HCI researchers and the practitioners who are consumers of research - and, if so, will endeavor to identify the dimensions of the problems and propose possible solutions. On the one hand, the workshop aims to articulate factors that may render the research literature inaccessible or irrelevant to practitioners and to suggest potential improvements. On the other hand, the workshop also aims to learn from researchers how their research could benefit from practitioner input.

Understanding the challenges faced during the management of data mining models

While the IT industry is moving forward with service-based solutions, they have left behind critical processes and soft IT assets unmanaged, especially at the intersection of business processes with Business Intelligence (BI). In this paper, we describe the challenges faced by model developers (or statisticians) and business analysts while managing data mining model assets of an organization that supports business processes in making real-time decisions and forecasts.

Brickstreams: physical hypermedia driven customer insight

Brickstreams is a system that employs hypermedia structures in physical spaces, specifically in brick-and-mortar retail environments, to capture insight into customer behavior. By instrumenting and tracking interactions with tagged products in retail stores, and by using demographic and location intelligence, our system is able to bring the benefits of online clickstream analysis to the brick and mortar world. We have implemented a prototype of Brickstreams, realized through REST web services and mobile device clients.

Designing dashboards for managing model lifecycle

Data mining is being extensively used for analysis of large data collections. While there is previous work on dashboard support for visual mining of the data, there is little or no work on dashboard support for managing the lifecycle (e.g. health) of the data mining models themselves. Issues such as quick performance decay, large scale deployments, collaborative use, and real-time business integration of models necessitate this type of support. In this paper, based on a year long study, we first describe the six stages of the model lifecycle and the preliminary design of the backend system that helps users manage mining models. Next, we discuss the three dimensions to be considered for dashboard visualization of the model lifecycle: introspection, customization, and presentation.