Product Bundling: Impacts of Product Heterogeneity and Risk Considerations

Bundling has been extensively studied in the literature and its benefits have been manifested through three perspectives of achieving better price discrimination, helping to save costs, and preserving the power for deterring a potential entrant. In this study, we examine two aspects of bundling which have not been studied before. We examine the impact of product heterogeneity on bundling decisions. We also address risk considerations in a bundling problem. Specifically, we consider a retailer who has the option of selling a bundle of two products (pure bundling policy), or selling the products separately (no-bundling policy). The retailer could also face a product selection problem for which we consider three scenarios of choosing two products with perfectly positively correlated, perfectly negatively correlated or independent reservation prices. We use a Mean-Variance approach to include retailer’s risk through her profit variability when maximizing the expected value of profit. We characterize the conditions under which a policy or scenario performs better than the others under the influence of product heterogeneity and/or retailer’s risk aversion. Among other findings, we show that optimal bundling price chosen by a risk-averse decision maker cannot be larger than the one chosen by a risk neutral decision maker

Modeling the association between EFL instructors' foreign language teaching enjoyment and humor styles

Background: Positive psychology in the field of applied linguistics has recently shifted its focus from L2 learners to L2 teachers as teachers have been revealed to be a pivotal external affordance for the emergence of learners' positive emotions such as enjoyment. Exploring the link between teacher-related constructs can provide deep insights into L2 teachers' emotional agency within L2 classroom context.Purpose: The current study seeks to examine the association between English as a foreign language (EFL) instructors' enjoyment of foreign language teaching (i.e., personal enjoyment, student appreciation, and social enjoyment) and humor styles (i.e., self-enhancing, affiliative, aggressive, and self-defeating humor styles).Materials and Methods: In order to examine this association, 244 (151 males and 93 females) Turkish EFL instructors voluntarily completed self-report scales measuring their foreign language teaching enjoyment and humor styles.Results: Results of the structural equation modeling (SEM) indicated that higher levels of student appreciation and social enjoyment are correlated with higher levels of affiliative and self enhancing humor. In addition, greater degrees of personal enjoyment, student appreciation, and social enjoyment are correlated with lower levels of aggressive humor, while self-defeating humor was unrelated to any of the enjoyment indices. There was also no significant gender difference for any humor styles.Conclusion: The findings are discussed in view of implications for teacher well-being

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

User interface patterns in recommendation-empowered content intensive multimedia applications

Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of “good” design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (“recommendations”) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, our work can stimulate future research bridging Recommender Systems, Web Engineering and Interface Design by means of Design Patterns, and highlights new research directions also discussed in the paper

An Integrated Outsourcing Framework: Analyzing Boeing’s Outsourcing Program for Dreamliner (B787)

This paper analyzes the outsourcing model which Boeing devised to develop its latest commercial airplane model: Dreamliner (B787). The development of this airplane which seemed to be very promising in the beginning turned into the longest delayed program in the history of the company. In this paper, we propose an integrated outsourcing framework through which we try to find the root causes of the delays and the resulted extra costs. The proposed framework shows how the interaction of all influential factors in four outsourcing dimensions (who, what, to whom, and how) determines the performance of an outsourcing program

Toward building a content-based video recommendation system based on low-level features

One of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For example, in the popular movie-sharing websites, such as Youtube, every-day, hundred millions of hours of videos are uploaded and big portion of these videos may not contain any meta-data, to be used by the system to generate recommendations. In this paper, we address this problem by proposing a method, that is based on automatic analysis of the video content in order to extract a number representative low-level visual features. Such features are then used to generate personalized content-based recommendations. Our evaluation shows that our proposed method can outperform the baselines, by producing more relevant recommendations. Hence, a set low-level features extracted automatically can be more descriptive and informative of the video content than a set of high-level expert annotated features

A Survey on Popularity Bias in Recommender Systems

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations today's recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and we review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey therefore includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. We furthermore critically discuss today's literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.Comment: Under review, submitted to UMUA

A survey on popularity bias in recommender systems

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations today’s recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey, therefore, includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss today’s literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.publishedVersio

Modeling the association between EFL students’ boredom and enjoyment: The mediating role of teacher humor style

This study sought to explore the association between English as a foreign language (EFL) students‟ foreign language learning boredom (FLB) and foreign language enjoyment (FLE) (i.e., personal enjoyment, teacher appreciation, and social enjoyment) as well as the mediating role of student-perceived teacher humor styles (i.e., affiliative, self-enhancing, aggressive, and self-defeating). In doing so, we firstly investigated the relationship between 229 (67 male and 162 female) Turkish EFL university students‟ FLB, FLE, and perceived teacher humor styles. We then probed to determine whether perceived teacher humor styles predict their FLB and FLE. The results indicated that all three indices of FLE had significant negative correlations with FLB. While affiliative and self-enhancing humor styles were significantly and positively correlated with FLE, they were negatively associated with FLB. Aggressive humor had only a significant negative correlation with the teacher appreciation subscale of FLE whereas self-defeating humor indicated a significant positive correlation with FLB. The results also showed that FLE could negatively predict FLB. Finally, the results of the mediation analysis indicated two significant mediation relationships which were significantly related to FLB through affiliative and self-enhancing humor. Implications are discussed in the context of teacher education