Balancing Leisure and Work: Evidence from the Seasonal Home

Seasonal homes are used during leisure time for many recreational activities, yet recent technological innovations have diminished the separation between the work place and the seasonal home. In a survey of Walworth County seasonal home owners, most who work full time report they seldom work during vacations and weekends from their seasonal home. Yet there is a distinct subgroup who do mix work into weekends and vacations for a variety of reasons. The most frequent reasons given by these people for working from the seasonal home were related to the expectations of coworkers and clients. Understanding more about the habits and motivations of those who frequently work during weekends and on vacations could provide a new perspective on the obstacles everyone faces in balancing work and leisure

Reliability Testing of the PABS (Pedestrian and Bicycling Survey) Method

The Pedestrian and Bicycling Survey (PABS) is a questionnaire designed to be economical and straightforward to administer so that it can be used by local governments interested in measuring the amount and purposes of walking and cycling in their communities. In addition, it captures key sociodemographic characteristics of those participating in these activities. Methods: In 2009 and 2010 results from the 4-page mail-out/mail-back PABS were tested for reliability across 2 administrations (test-retest reliability). Two versions--early and refined--were tested separately with 2 independent groups of university students from 4 universities (N = 100 in group 1; N = 87 in group 2). Administrations were 7 to 9 days apart. Results: Almost all survey questions achieved adequate to excellent reliability. Conclusions: Transportation surveys have not typically been tested for reliability making the PABS questionnaire an important new option for improving information collection about travel behavior, particularly walking and cycling

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

Towards Question-based Recommender Systems

Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations. Nevertheless, research in the field is still limited, compared to traditional recommender systems. In this work, we propose a novel Question-based recommendation method, Qrec, to assist users to find items interactively, by answering automatically constructed and algorithmically chosen questions. Previous conversational recommender systems ask users to express their preferences over items or item facets. Our model, instead, asks users to express their preferences over descriptive item features. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online by a closed-form solution based on the user answers. Meanwhile, our model infers the underlying user belief and preferences over items to learn an optimal question-asking strategy by using Generalized Binary Search, so as to ask a sequence of questions to the user. Our experimental results demonstrate that our proposed matrix factorization model outperforms the traditional Probabilistic Matrix Factorization model. Further, our proposed Qrec model can greatly improve the performance of state-of-the-art baselines, and it is also effective in the case of cold-start user and item recommendations.Comment: accepted by SIGIR 202