Query processing by, for, and of the masses: putting the user in the loop

今天的网络搜索仅限于用户向通用搜索引擎提交关键字搜索查询，然后筛选大量线性排序的答案。虽然这对于许多目的来说是令人满意的，但它忽略了Web 2.0的出现所激发的一个重要趋势，即用户搜索的内容信息与他们的社会概况（活动和联系）越来越多地集成在一起，从而产生了社会内容站点。首先，像del.icio.us和flickr这样的网站是以社交内容网站起家的：用户可以建立社交关系，标记内容项目，并与朋友分享。其次，越来越多的像亚马逊这样只提供内容的网站现在允许用户对商品进行标记/评级，并与朋友分享。同样，facebook等社交网站也允许用户与朋友分享内容。第三，虚拟社交内容网站有一个明确的趋势：例如，大多数新闻网站允许用户在MySpace上与朋友分享新闻文章。在所有这些社交内容网站中——无论是虚拟的还是真实的——都有丰富的结构化信息，包括内容项目和用户，以及用户提供的内容。把社交内容网站当作其他网站一样对待，就会错过利用这些网站中存在的重要内容和社交结构的机会。我们展望未来，与通用搜索引擎相比，特定领域的社交内容网站将越来越多地为用户提供有吸引力的选择。在这个项目中，我们将研究从信息检索风格搜索、数据库风格查询、推荐系统、社交网络、图形建模和查询、数据分析和数据挖掘丰富的社交和内容结构、多维查询和OLAP中提取的技术组合，以显着丰富和提高用户与社交内容网站集合交互的体验。用户将能够在异构的答案组之间无缝切换：这些组可能由不同粒度级别的内容项组成，或者“推荐”这些项的“相似”项，或者推荐这些项及其标签/评级的“相似”或专家用户，或者用户可能感兴趣的其他“主题”，导致信息发现的强大范例。

Today's web search is confined to users submitting a keyword search query to a generic search engine and sifting through a large linearly ordered list of answers. While this is satisfactory for many purposes, it ignores the significant trends, spurred by the advent of Web 2.0, toward increased integration of content information that users search for and their social profiles (activities and connections), leading to social content sites. First, sites like del.icio.us and flickr started as social content sites: users can form social ties and tag content items and share them with their friends. Second, more and more, content-only sites like Amazon are now allowing users to tag/rate items and share them with their friends. Similarly, social sites like facebook are allowing users to share content with their friends. Third, there is a definite trend toward virtual social content sites: e.g., most news sites let users share news articles with their friends in, say MySpace. In all these social content sites -- virtual and real -- there is rich structured information on both content items and users as well on user-provided content. Treating social content sites the same way as any other site misses out on leveraging the significant content and social structure present in these sites. We envision a future where domain-specific social content sites will increasingly offer an attractive alternative for users compared to generic search engines. In this program, we will investigate the combination of techniques drawn from information retrieval style search, database style querying, recommender systems, social networks, graph modeling and querying, data analysis and data mining over rich social and content structure, and multi-dimensional querying and OLAP, to significantly enrich and advance user experience in interacting with collections of social content sites. Users will be able to seamlessly shift between heterogeneous groups of answers: these groups may consist of content items at various levels of granularity, or "similar" items which "recommended" those items, or "similar" or expert users who recommended the items and their tags/ratings, or other "topics" that may interest the user, leading to a powerful paradigm for information discovery.