The majority of 18 to 44-year-old U.S. consumers preferred to binge-watch a whole series, according to 40 percent of respondents to a 2023 survey. In comparison, the share of viewers aged 45 years and older stating to do so was 29 percent, while most of the people interviewed in this age group said that this depends on the TV show.

The study from July 2020 consulted 859 marketers in the United States on their webinar viewing preferences. The report found that 52 percent of marketers said that they liked both forms of webinars, meaning live and on-demand webinars, while 29 percent of marketers enjoyed watching webinar recordings on-demand.

This statistic shows the results of a 2014 Popsugar survey among American women asking them what their viewing preferences are in the topics fashion, beauty and style online content. During the survey, 66.9 percent of female respondents said they prefer mainly images.

As of 2022, preferred viewing platforms among U.S. sports fans for watching live sports is generationally split. Among 55 to 64-year-old viewers, traditional broadcast television is strongly preferred for viewing over streaming services. The opposite is true among 18 to 34-year-olds, as well as overall U.S. viewers. Another large proportion of viewers across all age groups, however, reported that they do not have a preference for a viewing platform when tuning in to live sporting events.

Open Image Preferences

 Prompt: Anime-style concept art of a Mayan Quetzalcoatl biomutant, dystopian world, vibrant colors, 4K.



     Image 1



     Image 2







 Prompt: 8-bit pixel art of a blue knight, green car, and glacier landscape in Norway, fantasy style, colorful and detailed.



     Image 1… See the full description on the dataset page: https://huggingface.co/datasets/data-is-better-together/open-image-preferences-v1.

Recent research has shown that event-related brain potentials (ERPs) recorded while participants view lists of different consumer goods can be modulated by their preferences toward these products. However, it remains largely unknown whether ERP activity specific to a single consumer item can be informative about whether or not this item will be preferred in a shopping context. In this study, we examined whether single-item ERPs could reliably predict consumer preferences toward specific consumer goods. We recorded scalp EEG from 40 participants while they were viewing pictures of consumer goods and we subsequently asked them to indicate their preferences for each of these items. Replicating previous results, we found that ERP activity averaged over the six most preferred products was significantly differentiated from ERP activity averaged across the six least preferred products for three ERP components: The N200, the late positive potential (LPP) and positive slow waves (PSW). We also found that using single-item ERPs to infer behavioral preferences about specific consumer goods led to an overall predictive accuracy of 71%, although this figure varied according to which ERPs were targeted. Later positivities such as the LPP and PSW yielded relatively higher predictive accuracy rates than the frontal N200. Our results suggest that ERPs related to single consumer items can be relatively accurate predictors of behavioral preferences depending on which type of ERP effects are chosen by the researcher, and ultimately on the level of prediction errors that users choose to tolerate.

Introduction

We release the annotated data used in Dissecting Human and LLM Preferences. Original Dataset - The dataset is based on lmsys/chatbot_arena_conversations, which contains 33K cleaned conversations with pairwise human preferences collected from 13K unique IP addresses on the Chatbot Arena from April to June 2023. Filtering and Scenario-wise Sampling - We filter out the conversations that are not in English, with "Tie" or "Both Bad" labels, and the multi-turn… See the full description on the dataset page: https://huggingface.co/datasets/GAIR/preference-dissection.

Dataset Summary

The wise-data and wise-data-preferences datasets are synthetically created collections of values-laden conversations, designed to train language models to provide more nuanced and helpful responses to harmful, heavy, or exploratory questions. These datasets were specifically created to train the WiseLLama-8B model, a LLaMa-3.1-8B-Instruct model fine-tuned using SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization).

  Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/meaningalignment/wise-data.

Children express preferences for a wide range of options, such as objects, and frequently observe the preferences that others express towards these things. However, little is know about how these initial preferences develop. The present research investigated whether one particular type of social information – other children’s preferences – influences children’s own preferences. Four-year-old children observed, via video, two boys and two girls display the same preference for one of two stickers. Each child (peer) expressed liking for one sticker and dislike for the other. Then children completed two rounds of the Dictator Game, a classic resource distribution task. In each round, children distributed either 10 liked stickers or 10 disliked stickers (counterbalanced) between themselves and another child who was not present. If the preferences expressed by their peers influenced children’s own preferences, children should keep more of the liked than disliked stickers for themselves. In line with this prediction, more children kept more liked than disliked stickers, indicating their distribution patterns were influenced by their peers’ preferences. This finding suggests that children extracted informational content about the value of the stickers from their peers and used that information to guide their own preferences. Children might also have aligned their preferences with those of their peers to facilitate social bonding and group membership. This research demonstrates the strong influence of peers on children’s developing preferences, and reveals the effect of peer influence via video – a medium that young children are frequently exposed to but often struggle to learn from in other contexts.

Dataset Card for example-preference-dataset2

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/ashercn97/example-preference-dataset2/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/ashercn97/example-preference-dataset2.

This statistic shows the results of a 2017 survey in which Millennials were asked what type of watch they'd prefer to buy if they were given 5,000 GBP. During the survey, 64.3 percent of respondents said they would purchase a luxury watch, given the choice.

Rapidata Video Generation Alibaba Wan2.1 Human Preference

If you get value from this dataset and would like to see more in the future, please consider liking it.

This dataset was collected in ~1 hour total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.

  Overview

In this dataset, ~45'000 human annotations were collected to evaluate Alibaba Wan 2.1 video generation model on our benchmark. The up to date benchmark… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-wan2.1.

Rapidata Video Generation Runway Alpha Human Preference

If you get value from this dataset and would like to see more in the future, please consider liking it.

This dataset was collected in ~1 hour total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.

  Overview

In this dataset, ~30'000 human annotations were collected to evaluate Runway's Alpha video generation model on our benchmark. The up to date benchmark can… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-runway-alpha.

Dataset Card for dataset-preferences-llm-course-full-dataset

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/davanstrien/dataset-preferences-llm-course-full-dataset/raw/main/pipeline.yaml"

or explore the configuration: distilabel… See the full description on the dataset page: https://huggingface.co/datasets/davanstrien/dataset-preferences-llm-course-full-dataset.

There is a growing interest in understanding the factors that influence a user's perception and preferences for video quality. This study specifically focuses on how various factors, including video content, display settings, viewer characteristics, and the ambient environment, affect the subjective video quality assessment (VQA) of TV displays. To investigate these factors, two psychophysical experiments were conducted, and the results indicate that all four factors have a significant impact on video quality perception in different ways. This study is beneficial for researchers and developers who aim to improve display and environmental settings to provide viewers with the best possible viewing experience.

Research indicates that people continue to exhibit “wait-and-see” preferences toward climate change, despite constant attempts to raise awareness about its cataclysmic effects. Experiencing climatic catastrophes via simulation tools has been found to affect the perception of people regarding climate change and promote pro-environmental behaviors. However, not much is known about how experiential feedback and the probability of climate change in a simulation influence the decisions of people. We developed a web-based tool called Interactive Climate Change Simulator (ICCS) to study the impact of different probabilities of climate change and the availability of feedback on the monetary actions (adaptation or mitigation) taken by individuals. A total of 160 participants from India voluntarily played ICCS across four between-subject conditions (N = 40 in each condition). The conditions differed based on the probability of climate change (low or high) and availability of feedback (absent or present). Participants made mitigation and adaptation decisions in ICCS over multiple years and faced monetary consequences of their decisions. There was a significant increase in mitigation actions against climate change when the feedback was present compared to when it was absent. The mitigation and adaptation investments against climate change were not significantly affected by the probability of climate change. The interaction between probability of climate consequences and availability of feedback was significant: In the presence of feedback, the high probability of climate change resulted in higher mitigation and adaptation investments against climate change. Overall, the experience gained in the ICCS tool helped alleviate peoples' “wait-and-see” preferences and increased the monetary investments to counter climate change. Simulation tools like ICCS have the potential to increase people's understanding of climatic disasters and can act as a useful aid for educationalists and policymakers.

The global live TV streaming service market is experiencing robust growth, driven by increasing consumer demand for on-demand entertainment and cord-cutting trends. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. The proliferation of affordable high-speed internet access globally has broadened the reach of streaming services, making them accessible to a wider audience. Furthermore, the increasing availability of diverse content packages, including live sports, news, and entertainment channels tailored to specific viewing preferences, has significantly boosted market appeal. The rise of smart TVs and media streaming devices further contributes to market growth by seamlessly integrating streaming services into consumers' daily routines. Competition among established players and emerging newcomers continues to foster innovation and drive down prices, benefiting consumers and accelerating market penetration. However, challenges remain. Retaining subscribers in a highly competitive landscape requires continuous investment in content acquisition and technological advancements. The impact of economic downturns on consumer spending patterns presents a potential restraint. Furthermore, regional variations in internet infrastructure and consumer preferences require tailored strategies for effective market penetration. Nevertheless, the long-term outlook for the live TV streaming service market remains positive, driven by ongoing technological innovations and evolving consumer viewing habits. The segmentation of the market by subscription type (cable TV, wireless antenna) and application (mobile, web, streaming devices) allows companies to target specific demographics effectively. Key players such as YouTube TV, Hulu, and others are actively involved in shaping this dynamic market through aggressive content acquisition, partnerships, and technological improvements. The geographical distribution reveals that North America currently holds a significant market share, but growth opportunities are substantial in developing economies in Asia-Pacific and other regions.

Dataset Card for H4 Stack Exchange Preferences Dataset

  Dataset Summary

This dataset contains questions and answers from the Stack Overflow Data Dump for the purpose of preference model training. Importantly, the questions have been filtered to fit the following criteria for preference models (following closely from Askell et al. 2021): have >=2 answers. This data could also be used for instruction fine-tuning and language model training. The questions are… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences.

Funk, Patricia, (2016) "How Accurate are Surveyed Preferences for Public Policies? Evidence from a Unique Institutional Setup." Review of Economics and Statistics 98:3, 442-454.

Dataset Card for fine-preferences-magpie-generated-system-prompt-v1

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/fine-preferences-magpie-generated-system-prompt-v1/raw/main/pipeline.yaml"

or explore… See the full description on the dataset page: https://huggingface.co/datasets/distilabel-internal-testing/fine-preferences-magpie-generated-system-prompt-v1.