Signed Distance-based Deep Memory Recommender

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models

Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour

Rapport, the close and harmonious relationship in which interaction partners are "in sync" with each other, was shown to result in smoother social interactions, improved collaboration, and improved interpersonal outcomes. In this work, we are first to investigate automatic prediction of low rapport during natural interactions within small groups. This task is challenging given that rapport only manifests in subtle non-verbal signals that are, in addition, subject to influences of group dynamics as well as inter-personal idiosyncrasies. We record videos of unscripted discussions of three to four people using a multi-view camera system and microphones. We analyse a rich set of non-verbal signals for rapport detection, namely facial expressions, hand motion, gaze, speaker turns, and speech prosody. Using facial features, we can detect low rapport with an average precision of 0.7 (chance level at 0.25), while incorporating prior knowledge of participants' personalities can even achieve early prediction without a drop in performance. We further provide a detailed analysis of different feature sets and the amount of information contained in different temporal segments of the interactions.Comment: 12 pages, 6 figure

Chatbot Catalysts: Improving Team Decision-Making Through Cognitive Diversity and Information Elaboration

As the integration of artificial intelligence (AI) into team decision-making continues to expand, it is both theoretically and practically pressing for researchers to understand the impact of the technology on team dynamics and performance. To investigate this relationship, we conducted an online experiment in which teams made decisions supported by chatbots and employed computational methods to analyze team interaction processes. Our results indicated that compared to those assisted by chatbots in later phases, teams receiving chatbot assistance during the initial phase of their decision-making process exhibited increased cognitive diversity (i.e., diversity in shared information) and information elaboration (i.e., exchange and integration of information). Ultimately, teams assisted by chatbots early on performed better. These results imply that introducing AI at the beginning of the process can enhance team decision-making by promoting effective information sharing among team members

Word of Mouth, the Importance of Reviews and Ratings in Tourism Marketing

The Internet and social media have given place to what is commonly known as the democratization of content and this phenomenon is changing the way that consumers and companies interact. Business strategies are shifting from influencing consumers directly and induce sales to mediating the influence that Internet users have on each other. A consumer review is “a mixture of fact and opinion, impression and sentiment, found and unfound tidbits, experiences, and even rumor” (Blackshaw & Nazarro, 2006). Consumers' comments are seen as honest and transparent, but it is their subjective perception what shapes the behavior of other potential consumers. With the emergence of the Internet, tourists search for information and reviews of destinations, hotels or services. Several studies have highlighted the great influence of online reputation through reviews and ratings and how it affects purchasing decisions by others (Schuckert, Liu, & Law, 2015). These reviews are seen as unbiased and trustworthy, and considered to reduce uncertainty and perceived risks (Gretzel & Yoo, 2008; Park & Nicolau, 2015). Before choosing a destination, tourists are likely to spend a significant amount of time searching for information including reviews of other tourists posted on the Internet. The average traveler browses 38 websites prior to purchasing vacation packages (Schaal, 2013), which may include tourism forums, online reviews in booking sites and other generic social media websites such as Facebook and Twitter.Peer reviewedFinal Accepted Versio