Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page

The Broadband Difference

Presents findings from a survey conducted in January and February 2002. Examines how online Americans' behavior and level of satisfaction with the Internet changes with high speed Internet connections at home

A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems

In order to sustain the user-base for a web service, it is important to know the return time of a user to the service. In this work, we propose a point process model which captures the temporal dynamics of the user activities associated with a web service. The time at which the user returns to the service is predicted, given a set of historical data. We propose to use a Bayesian non-parametric model, log Gaussian Cox process (LGCP), which allows the latent intensity function generating the return times to be learnt non-parametrically from the data. It also allows us to encode prior domain knowledge such as periodicity in users return time using Gaussian process kernels. Further, we cap- ture the similarities among the users in their return time by using a multi-task learning approach in the LGCP framework. We compare the performance of LGCP with different kernels on a real- world last.fm data and show their superior performance over standard radial basis function kernel and baseline models. We also found LGCP with multitask learning kernel to provide an improved predictive performance by capturing the user similarity

Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06

The Behavioral Code:Recommender Systems and the Technical Code of Behaviorism

Our lives are increasingly mediated, regulated and produced byalgorithmically-driven software; often invisible to the people whose lives it affects.Online, much of the content that we consume is delivered to us through algorithmic recommender systems (“recommenders”). Although the techniques of such recommenders and the specifc algorithms that underlie them differ, they share one basic assumption: that individuals are “users” whose preferences can be predicted through past actions and behaviors. While based on a set of assumptions that may be largely unconscious and even uncontroversial, we draw upon Andrew Feenberg’s work to demonstrate that recommenders embody a “formal bias” that has social implications. We argue that this bias stems from the “technical code” of recommenders – which we identify as a form of behaviorism. Studying the assumptions and worldviews that recommenders put forth tells us something about how human beings are understood in a time where algorithmic systems are ubiquitous. Behaviorism, we argue, forms the episteme that grounds the development of recommenders. What we refer to as the “behavioral code” of recommenders promotes an impoverished view of what it means to be human. Leaving this technical code unchallenged prevents us from exploring alternative, perhaps more inclusive and expansive, pathways for understanding individuals and their desires. Furthermore, by problematizing formations that have successfully rooted themselves in technical codes, this chapter extends Feenberg’s critical theory of technology into a domain that is both ubiquitous and undertheorized

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge. We consider podcasting to be an emerging medium with rapid growth in adoption, and discuss challenges that arise when applying traditional recommendation approaches to address the cold-start problem. Using music consumption behavior, we examine two main techniques in inferring Spotify users preferences over more than 200k podcasts. Our results show significant improvements in consumption of up to 50\% for both offline and online experiments. We provide extensive analysis on model performance and examine the degree to which music data as an input source introduces bias in recommendations.Comment: SIGIR 202

Cannabis use frequency and mood on creativity

This study examines the relationship between cannabis use (infrequent, moderate, and heavy use) and one’s mood (neutral, positive, and negative) on creativity. Folk ideas of creativity and the relationships between cannabis use and mood may not reflect the real relationship between these factors (e.g. regarding cannabis use, it is perceived to be linked with higher rates of creativity; regarding mood, negative states [i.e. tortured artist] are thought to fuel creativity). Although both cannabis use and mood have been found to influence creativity independently, the current study is unique in its aims to identify whether cannabis use and mood interact to influence one’s creativity. Participants (n=242) engaged in a creativity task over three different mood blocks (neutral, positive, and negative), where mood was induced via sound stimuli. Creativity was measured by the number of alternative uses for common objects produced by the participants in the alternative use task (AUT). The AUT was followed by a cannabis use survey and the Creative Achievement Questionnaire (CAQ). Although no significant interaction or main effects of cannabis use frequency and mood was found, post hoc analysis of the survey data suggest self-report creativity and one’s education level are linked to higher rates of creativity. Post-hoc analyses also suggest that heavy cannabis users reported a higher CAQ score, thus higher lifetime creativity. Limitations to this study include a failed manipulation check of mood inducement. Future research directions and implication of this study will be discussed

The influence of national culture on the attitude towards mobile recommender systems

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation of Korea Grant funded by the Korean governmen