Joel Liu's Library tagged discovery →  View Popular, Search in Google

Crowdsourced Serendipity

Bridging the gap between editorial and social serendipity, the notion of crowdsourced relevance really only delivers a broad, lowest-common-denominator level of content discovery. While not without its usefulness to the degree that we want to be aware of what is most popular and most talked about, the trade-off is the lack of personalization.

The pro here is the viral component, which makes up a great deal of our online content-discovery routines. Crowdsourced serendipity provides a tier of distribution in touch with a larger zeitgeist, from trivial cat videos to important broad-based news. The downside is that the lowest common denominator lacks any precision and therefore has limited utility.

To some extent, this recognition is obvious. Our interests change and evolve over time. Yet for the kind of precision that seeks to provide consistent serendipity in the ways we have been discussing, the indicators need to be equally sensitive.

The content that I want, and better yet, the content that I don’t even know that I want, is an ever-changing proposition based on any number of factors. To achieve that level of sophisticated customization requires a sensitive understanding of context for any proposed “serendipity engine”, both a context of the content and the user.

Search


• Conceptual inputs - as semantic metadata on the web improves, faceted searching and filtering by selecting combinations of structured topics, such as people, places, events, things, and ideas will be increasingly possible
• Audible inputs - already seen in emerging services like Google Voice Actions, Siri, and Shazam, you will be able to use your voice, a clip from a song, and other audio sources as a way to run a search and identify content
• Visual inputs - already seen in emerging services like Google Goggles, Google Similar Images, and Like.com, you will be able to use images and videos, as a way to search for similar content

Results


• Filtering - will enable rapidly narrowing down results, smoothly focusing in on relevant content, removing irrelevant content from view. Microsoft's Pivot is good example of how this can work and how powerful this can be
• Visualizing - richer interfaces for presenting content results, such as result data populated into dynamically generated, interactive infographics or visualizers, will enable content to be more naturally consumed and understood

• 19. FILE BASED STORAGE SEES THEWORLD AS A SET OF NESTED COLLECTIONS OF ISOLATED INFORMATION
• 20. LIKE FILING CABINETS
• 21. OR A WAREHOUSE

• 22. RELATIONAL DATABASES AS HIGHLY ORGANISED COLLECTIONS OF INFORMATION WHICH INTERSECT
• 23. LIKE ENROLMENT
• 24. LIKE BANKING
• 25. OR AN OCD LARDER

• 56. WHAT SERENDIPITY ISN’T!• Random; random combinations of information are just noise. putting teflon on a dolphin’s nose would not be a useful contribution to society. Don’t confuse unexpected with random!• Accidental; serendipitycomes from an attentive, and often intuitive mind receiving diverse information.• Luck; serendipity is a cognitive process that creates new connections between previously unrelated concepts and realises the value in them.
• 57. THREE STEPS TO SERENDIPITY• Remove Isolation. Relationships are low cost and can be added to data at any point, so create them and create as many as possible ignoring contextual or semantic boundaries.• UseMultiple Hops. Cross semantic and contextual boundaries when providing relevancy.• Weight and Filter. The value of the information found should relate to the route traversed. Allow users to manually pass on information to others.
• 58. CODING SERENDIPITY How can we add serendipity into our systems?• Information must be able to travel freely between users.• Information should be able to travel multiple levels of indirection with ease.• Information should have the maximum number of inter- connections across semantic boundaries.• Information relationships should be categorised and potentially contain meta-data required for weighting.

• 59. HOW NEO4J HELPS• Relationships are created trivially at low cost at any time with no regards to semantic boundaries.• Connected information over many hops can be retrieved quickly using Node#traverse or the Traversal framework.• Relationships can have both types and properties making weight and filter calculations easy.
• 60. TAKE AWAY• Create more relationships.• Let information cross contextual and semantic boundaries.• Make sure relevancy is probabilistic, not deterministic.• Serendipity is not accidental, random or lucky!• Themore heterogeneous and connected your data becomes, the more you should consider Neo4j.