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Content tagging and recommender systems
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Content tagging and recommender systems Content tagging and recommender systems Presentation Transcript

  • Capstone Report Presentation User-Generated Metadata in Social Software: An Analysis of Findability in Content Tagging and Recommender Systems Presented by Michael Barnes In partial fulfillment of the requirement for the degree of Master of Science. March, 2007
  • ABSTRACT
    • This study describes how user-generated metadata may be leveraged to enhance findability in web-based social software applications (Morville, 2005). Two interaction design systems, content tagging (Golder & Huberman, 2005) and recommender systems (Resnick & Varian, 1997), are examined to identify strengths and weaknesses along three findability factors : information classification , information retrieval and information discovery .
    • Greater overall findability strength may be found in content tagging systems than in recommender systems.
  • Introduction
    • Before I present the results and outcomes of the study, I thought I’d provide some history as to how I became interested in this topic.
      • 5 or 6 years ago I began to notice a trend on a few commercial web sites (namely amazon and google) that began placing recommendations around my browsing and search activities.
  • Introduction
      • I realized that recommendations were being presented in contextually relevant moments of my browsing experience as strategies to cross sell products or simply as a device to enable broader information awareness.
      • The system was learning from my navigation and browsing behavior to make personalized recommendations.
      • The presentation layer of the web experience was evolving from a static, outbound communications tool to a more dynamically created user-centric experience.
      • Despite these dynamic enhancements, the communication channel was still primarily one directional – outbound. The user was the recipient of information and did not overtly participate.
  • Introduction (Continued)
      • More recently, I discovered a web site – del.icio.us that allows users to apply their own descriptive labels to web sites.
      • It is a book marking tool – like the one built into every web browser – but allows for user-generated metadata so that bookmarks can live in more than one folder and have multiple terms attached to them to enhance their retrieval.
  • Introduction (Continued)
      • I noticed two key things happening with content tagging systems – user control for improved labeling and findability and the benefit of surfacing the tags of other users to create access points to related content.
  • TIME-liness of Topic Since then, the rise of social software designed to leverage user-generated metadata has boomed. The 2006 Time Magazine’s “Person of the Year” was - the user! The rapid increase of user participation and user generated content in social software systems continues to gain popularity under the moniker of “Web 2.0.”
  • TIME-liness of Topic “ It's a story about community and collaboration on a scale never seen before. It's about the many wresting power from the few and helping one another for nothing and how that will not only change the world, but also change the way the world changes. It's a tool for bringing together the small contributions of millions of people and making them matter. And for seizing the reins of the global media, for founding and framing the new digital democracy, for working for nothing and beating the pros at their own game, TIME's Person of the Year for 2006 is you.”
  • Key Definitions
    • Information System
    • A web-based information system consists of organization, labeling, navigation, and searching sub-systems that help people find and manage information more successfully (Morville & Rosenfeld, 2002).
    • Metadata
    • Structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource. Metadata is often called data about data or information about information (NISO, 2004).
  • Key Definitions (Continued)
    • Social Computing
    • Social computing is the use of social software to create social conventions and contexts in a virtual, web-based community (Millen and Patterson, 2002).
    • Social Software
    • Tools (software) designed to enable peer-to-peer interactions in order to facilitate content creation and collaboration through computer-mediated communication. It enables a “bottom-up” approach where users of the software create the system’s value rather than the value being delivered by an external source (Tepper, 2003).
  • Key Definitions (Continued)
    • User-Generated Metadata
    • Metadata that is defined and described by human users based on individual preferences rather than by structured content classification or management requirements such as taxonomies or controlled vocabularies (Golder and Huberman, 2005). Metadata that is created in a “bottom-up” method by users of a social software system. It is unstructured because it is not predefined by a formal content management system using a controlled vocabulary, taxonomy, ontology, or other structured categorization process (Mathes, 2004).
  • Key Definitions (Continued)
    • Findability
    • For purposes of this study, findability refers to the likelihood of retrieving desired information in a web-based system. It is the quality of being locatable or navigable. At the item level, we can evaluate to what degree a particular object is easy to discover or locate. At the system level, we can analyze how well a physical or digital environment supports navigation and retrieval. Information that is highly findable is easily located and accessed through the use of a well developed information retrieval system (Morville, 2005).
  • Key Definitions (Continued)
    • Content Tagging Systems
    • Web-based information systems that enable users to assign uncontrolled keywords to information resources. Such tags are used to enable the organization of information within a personal information space, but are also shared, thus allowing the browsing and searching of tags attached to information resources by other users. It also allows users to tag their information resources with those tags that exemplify popularity.
    • Tags are generally single terms, however the assignation of multiple tags to a single resource can be accommodated by omitting essential syntax or punctuation and by using symbols to combine terms (e.g. information+management) (Macgregor and McCulloch, 2006).
  • Key Definitions (Continued)
    • Recommender Systems
    • Recommender systems capture implicit and explicit user decisions and behavior in order to recommend content that matches previous choices. Most recommender systems rely on collaborative filtering software that leverage algorithmic models to generate predictive recommendations. Recommender systems use the opinions of a community of users to help individuals in that community more effectively identify content of interest from a potentially overwhelming set of choices (Resnick & Varian, 1997).
  • Key Definitions (Continued)
    • Information Classification
    • The process of grouping information into intuitive categories by applying descriptive metadata that may be used as part of an organization schema designed to aid information retrieval and discovery (Mathes, 2004).
    • Information Discovery
    • The process of retrieving useful information through serendipitous means (Morville and Rosenfeld, 2002) – often due to classification descriptions provided by other users in ambiguous organization systems such as folksonomies (Mathes, 2004).
    • Information Retrieval
    • The process of locating desired information through the use of web based search utilities (Morville, 2004).
  • Key Definitions (Continued)
    • Serendipitous Information Discovery
    • In search systems, serendipitous discovery is the process of retrieving information related to an initial search query due to the presentation of related information in the search results. This occurs most often in ambiguous classification schemes where information is likely to exist in multiple categories depending on the context of the search. The grouping patterns of ambiguous organization schemes support associative learning that enables users to identify new and unintended relationships about the information they are seeking (Morville and Rosenfeld, 2002).
  • Summary of Key Results
      • Based on an accounting of the strength and weakness elements in each of the six system-factor combinations, the most oft-cited strength of content tagging systems is the ability to classify information.
      • The most oft-cited strength of recommender systems is the ability to discover information.
      • The most consistent weakness of content tagging systems is the ability to classify information.
      • The most oft-cited weakness of recommender systems is the ability to classify information.
      • One interpretation of this pattern is that content tagging systems enable greater findability potential than recommender systems. However, this statement would need to be evaluated further using more quantitative measures to ensure statistical relevancy.
  • Results (Continued)
      • Further, the overall strength/weakness balance for all content tagging factors is +5 while the overall strength/weakness balance for all recommender system (RS) factors is -7.
      • Based on this accounting approach, content tagging systems may be assumed to provide greater overall findability strength than recommender systems due to the number of overall findability strengths minus overall weaknesses per the results of the study
  • Presentation of Outcome
    • The outcome of the study is designed to help user experience professionals make informed decisions regarding the potential findability strengths and weaknesses of each system.
    • The results and outcome were grouped by system-findability factor combinations
    • Findability strengths and weaknesses are presented for each combination
  • Findability Strengths - Information Classification
    • Strengths of Content Tagging & Information Classification
      • User-classified content is personally and contextually relevant
      • Captures unique user and community generated vocabulary, specialized terminology, and jargon
      • Easy to use - low barrier to entry, low cognitive/learning costs
      • Incentive – Users typically classify for their own personal needs while their tags benefit all system users.
    • Strengths of Recommender Systems & Information Classification
      • Classification based on overt user selections, preferences, rankings, and previous behavior
  • Findability Weaknesses - Information Classification
    • Weaknesses of Content Tagging & Information Classification
      • Uncontrolled, ambiguous, and unreliable categorization
      • Requires a large population of classifiers to generate useful results
      • Allows for classifier term ambiguity – spelling errors, multiple word tag descriptions, and personal notes such as “todo” “toread”
    • Weaknesses of Recommender Systems & Information Classification
      • Machine/algorithm-based – lacks contextual, human classification
      • Difficult and expensive to design, configure, deploy, and maintain
      • Lack of incentive – classification prompts require incentive for the user to provide preferences/feedback
  • Findability Strengths - Information Retrieval
    • Strengths of Content Tagging & Information Retrieval
      • Feedback - tight feedback loop (users see results of tagging immediately)
      • Provides breadth and depth/precision and recall simultaneously
      • Specialized or trusted content tags and users emerges through tag pattern visibility
    • Strengths of Recommender Systems & Information Retrieval
      • Transparency of criteria used to generate recommendation can generate trust, adoption, and usage
      • Contextual/ephemeral personalization – recommendations are pushed based on relevant personal inputs
  • Findability Weaknesses - Information Retrieval
    • Weaknesses of Content Tagging & Information Retrieval
      • Search results lack precision of specialized search engines or Boolean searches
      • Breadth of recall (related tags) may be overwhelming (distract with cognitive noise)
    • Weaknesses of Recommender Systems & Information Retrieval
      • Limited Accuracy/Incorrect Recommendations
      • Inflexible design – recommendations are generated by algorithmic logic and lack human inputs
  • Findability Strengths - Information Discovery
    • Strengths of Content Tagging & Information Discovery
      • Multiple access/pivot points to related content
      • Locate other users/groups with related interests and content – aids community building/social networking
      • Supports natural browsing – berrypicking – over searching
    • Strengths of Recommender Systems & Information Discovery
      • Enables serendipitous browsing and information discovery
      • Pushes Passive/Organic Recommendations
      • Crates implicit and explicit social connections – matching users builds community
  • Findability Weaknesses - Information Discovery
    • Weaknesses of Content Tagging & Information Discovery
      • Lacks expert or specialized results managed by domain experts
      • Competing groupings, categories, and terms
      • Unorganized presentation of information may be time consuming to comprehend
    • Weaknesses of Recommender Systems & Information Discovery
      • Inaccurate recommendations
      • Task intrusiveness from recommendations placed in user interface
      • Loss of user trust and loyalty if recommender techniques or rationale for recommendations not disclosed
  • Conclusion
      • As outlined in the outcome slides, there are clear strengths and weaknesses to be considered when designing a content tagging or recommender system that leverages user-generated metadata as a mechanism to enhance findability.
      • Content tagging provides more user incentive (lower barrier to participation) and overall findability benefits than recommender systems.
      • Search engines great for precision, content tagging good for both precision and recall, but only within the community of taggers who have evaluated content.
      • The best content bubbles up – as in google, but in CT systems, there are links to less popular, but potentially significant – related content.
  • Thank You…and Examples
    • The following slides include screenshots of common content tagging and recommender systems.
    • The screen shots highlight various UI design strategies for user input – prompts to solicit user-generated metadata.
    • By no means an exhaustive list of examples. Many ways to implement and design inputs for user-generated content.
  • Recommender System Examples
    • Amazon.com
  • Recommender System Example
    • Amazon.com
  • Recommender System Example
    • Amazon.com
  • Recommender System Example
  • Tagging and Recommender System Examples
    • Amazon.com
  • Recommender System Example
    • Netflix.com
  • Recommender System Example
    • Netflix.com
  • Recommender System Example
    • Netflix.com
  • Recommender System Example
    • Netflix.com
  • Recommender System Example
    • Netflix.com
  • Recommender System Example
    • Yahoo Music Player
  • Content Tagging Example
    • Citeulike.org
  • Content Tagging Example
    • Citeulike.org
  • Content Tagging Example
    • Citeulike.org
  • Content Tagging Example
    • Citeulike.org
  • Content Tagging Example
    • Del.icio.us
  • Content Tagging Example
    • Del.icio.us
  • Content Tagging Example
    • Yahoo’s “MyWeb” Search with Tagging
  • Content Tagging Example
    • Yahoo’s “MyWeb” Search with Tagging
  • Recommender System Example
    • Pandora.com
  • Recommender System Example
    • Pandora.com
  • Recommender and Content Tagging
    • Youtube.com