SBIR Phase I: The Echo Nest Music Personalization

这个小型企业创新研究（SBIR）第一阶段项目旨在解决个性化音乐搜索和推荐的计算问题。近年来，数字音乐的爆炸式增长迫切需要强大的知识管理技术和工具。由于音乐内容和感知的高度主观性，最好的搜索策略是根据每个人的品味和偏好，结合文化和声学描述，以个性化的方式对媒体进行排名。Echo Nest的预测个性化技术使用无监督数据挖掘和机器监听技术，以协作和自动方式计算和收集在线文化观点和声学内容。将音乐的文化和声学概念与对个人收听模式、评级和反馈的分析结合起来，形成了一个垂直搜索/推荐引擎，它了解内容、社区的反应和用户的偏好。智能音乐个性化超越了搜索和推荐。因为这种方法是完全自主和可扩展的，它可以有效地解决独立音乐的长尾以及公告牌100;发现艺术家和利基或预测趋势和热门;直接向个人销售独立音乐，并优化聚合器，分销商和唱片公司的选择。Echo Nest引擎是纯文本搜索引擎的感知媒体补充，具有巨大的市场潜力。

This Small Business Innovation Research (SBIR) Phase I project aims at solving the computational problem of personalizing music search and recommendation. The recent explosion of digital music has created an urgent need for powerful knowledge management techniques and tools. Because of the highly subjective nature of musical content and perception, the best possible search strategy would rank media in a personalized fashion, based on each individual's tastes and preferences, from combined cultural and acoustic descriptions. The Echo Nest's predictive personalization technology computes and collects, collaboratively and automatically, cultural opinions online and acoustic content using unsupervised data mining and machine listening techniques. Combining cultural and acoustic notions of music together with the analysis of an individual's listening patterns, ratings and feedback, leads to a vertical search/recommendation engine that knows about content, communities' reaction, and users' preferences. Intelligent music personalization goes beyond search and recommendation. Because the approach is fully autonomous and scalable it can efficiently address the long tail of independent music as well as the Billboard 100; discover artists and niches or predict trends and hits; market indies directly to individuals and optimize aggregators, distributors, and record labels' selection. The Echo Nest engine is the perceptual-media complement to purely text-based search engines and has a significant market potential.