Victus Spiritus


Semantic bots that learn what you like

22 Dec 2009

Imagine the information you publicly shared was crawled by more than search engines. A new type of information gathering bot is about to begin accessing your twitter streams. At Victus Media we're experimenting with semantic extracted tags that accurately capture the essence of status updates. So far the relevancy in our personal tests is accurate over half the time, and needs minor adjustments for many of the mismatches. But we're at a design impass on the Intelligent Media Manager, as there are many development paths which lead to improved performance. One long term goal is to construct personalized search assistants tuned to each user. What we work on next will highly be a function of what users find most appealing to fascilatate our longer term development path.

Our First Marketing Campaign

It's time for Victus Media to get serious user input. And to achieve that we've decided to ratchet up our visibility by sending single tweets to users with a simple message, an extracted tag, and an opt out choice (to never receive further messages). Spam is not something my cofounder or I support but "cold calling" is a necessary evil of obtaining valued user feedback. At the very least, we'll determine what fraction of users are open to the idea of supporting the development of our take on social semantic search. Our best hopes are for rich user feedback and common desired features as we move forward. Worst case we'll make some folks angry for attempting to reach out.

Serendipity from Semantic Search

Meeting new people connected by interest, or discovering a peer reviewed topic expert is a tangible benefit of social media. Dynamic user association by leveraging the IMM database is our first attempt at connecting users by topic.

An early idea is to match a person's tags with other folks that share common interests. In this way we hope to prompt conversations and new friendships.

Another concept is to relate a users tags to dominant tags in our database, and highlight the match between trending topics and what a user is really interested in.

Our Marketing Agent

@semantic2 is our current agent. It randomly processes a users tweet and sends them an appropriate message every 5 minutes (we overloaded our server and got one Twitter account frozen for sending messages too fast).