Everyone defines Social Search differently so I’ll start out with my own definition. I define social search to be the intersection of Search and Social Networking.
Search is reasonably well understood. Basically as more information becomes digitized we need efficient tools to index and access this information. The Social Network part effectively takes the way we commune and interact in the real world and amplifies this by moving online.
The interesting thing is that the intersection of these two areas should be reasonably well defined. For example, if the space was ‘Search Social’ instead of ‘Social Search’ we would expect to use more efficient index and access tools to effect the way we socialize online. I think we already see that happening extensively. We use search tools to obtain composite digital profiles before and after we meet people. Also, think about some of the dating sites and the amount of information (increasingly psychographic) that is stored and filtered prior to ‘dating’
So if social search (or social networking search) followed the same definition rules, it would logically take the way we commune and interact offline, move this online and then apply this result to enable more efficient information indexing and retrieval for each of us.
So the logical result is that search becomes:
· More communal (people-based)
· More personal
This would lead to a set of potential social attributes where search results are based on:
1. your previous searching history
2. your stated preferences
3. The search history of ‘trusted’ relationships
4. The stated preferences of ‘trusted’ relationships
5. The search history of individuals ‘similar’ you
6. The stated preferences of individuals ‘similar’ to you
7. The search history of an ‘affinity’ group that a person belongs to
8. The stated preferences an ‘affinity’ group that a person belongs to
9. The stated preferences of ‘experts’ in a particular area
10. The search history of ‘experts’ in a particular area
11. The stated preferences of a ’group of experts’ in a particular area
12. The search history a ’group of experts’ in a particular area
13. The universe of search history
14. The universe of stated preferences
Lower numbers are the more ‘personal’ search attributes, higher numbers are more ‘communal’ search attributes.
We would then use these social search attributes or dynamic pattern recognition to make search results increasingly ‘meaningful’ in each specific context.
As social search evolves, I think we will start to see models emerge that will have increasingly accurate weightings of these and other social search attributes. In addition the user will have the ability to adjust the attribute weightings to reflect context or individual preference. Finally social search algorithms will be able to adjust attribute based on the term that it being search for. For example, a search for ‘movies’ might be more heavily weighted to stated preferences, people with similar tastes and experts. However, a search for travel might skew towards stated preferences and trusted relationships. The former is a relatively low risk endeavor but the latter has greater consequences and therefore you may only trust your own preferences and the preferences of those individuals with whom you have a trusted relationship.
Here at Customerforce, we use all of these attributes. We also go further and filter all of the content based on either:
· Affinity group
· Trusted relationships
· Your stated preferences
· The Customerforce universe
The connection to a person’s preferences can be either active or passive and implicit or explicit.
In summary, it will be fascinating and exciting to see how social search evolves. We are committed to an extraordinarily high level of innovation in this nascent space. This will be a great place to document Customerforce’s approach to social search and over the next few weeks I’ll compare and contrast this with the approach of our peer group…
Recent Comments