Digg's filtering value is eroding now that it has become mainstream. It is time for YAM (yet another mashup) Web 2.0 style - mashing digg and social networking to deliver the ability to see one's social network's diggs, and more broadly to personalize any variety of filters for digg. Maybe some companies will even present this at Web 2.0 Launch Pad this week.
Del.icio.us has the same "diluted value due to size" challenge, and is including an early social network tool with "my network," however my network is discrete. A better implementation would be filters overlaying the whole site's functionality, allowing users to filter on all, my networks, me, and any other affinity profiles.
Affinity Ratings and Mashups 2.5
Even more useful than mashing up social networks and digg and bookmarking would be adding user-specified Affinity Ratings to digging and tagging. Users could select canned affinity profiles (e.g.; green, sustainable, entrepreneur-posted/created) or create their own - the "Jane Geek Affinity Rating." The easiest implementation of Affinity Ratings would be aggregating tags.
Digg functionality could be expanded to allow personalized affinity ratings on news, and next and more importantly, on non-news items such as products and services.
Sunday, November 05, 2006
Affinity Ratings - New gigg for Digg?
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2 comments:
Hi Michael, thanks for the comment and note about last.fm I am suggesting the idea of multiple filters so you can quickly flip between views to discover the wisdom-of-crowds-as-a-whole, to my-singularity-friends, to my-work-friends, to my own. Digg's so big that sub-networks can provide additional value. I think the next step, crowd-marked Affinity Ratings, could be huge.
Please comment on the OPINE project that was mentioned in an article in the front section of the Saturday Mercury News:
OPINE is an unsupervised information extraction system which mines product review data in order to build a model of important product features, their evaluation by reviewers and their relative quality across products. First, the system automatically identifies product features, both explicit and implicit. For example, the sentence "Our room's temperature was just right" mentions the explicit feature "RoomTemperature" whereas the sentence "The hotel is ridiculously expensive" refers to the implicit feature "HotelPrice". Second, the system identifies opinions regarding product features and establishes their polarity. For example, "fantastic" is a positive opinion, whereas "disappointing" is a negative opinion. Finally, the system ranks opinions corresponding to the same feature based on their strength. For example, "great" is stronger than "almost great" which in turn is stronger than "mostly ok".
http://www.cs.washington.edu/homes/amp/opine/
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