March 12th, 2010
Affinity-based browsing, the way I envision it, goes beyond the shallow targeting of Netflix, Amazon, or, from what I can tell, Glue. These programs base recommendations on, for example, people who viewed Ice Age also viewed Cars, so the system recommends Cars. Stumbleupon seems to address general similarities, but doesn’t approach the type of targeting I’m suggesting, because its “classification” and “clustering” engines are primarily based on subject matter and lifestyle interests, which is a start, but isn’t enough to truly serve targeted information. Stumbleupon also collects sex and age information, but it doesn’t know other demographic information, such as where I live or my education level. Thus, the groups I’ll be placed in will be very broad, even though organized around a particular interest.
I’d like more variables in the formula. Advertisers seem to know a lot about us, since ads often seem to be well-targeted (feel free to test this claim by entering any well-advertised pharmaceutical product into your search engine, or use “diet” as a keyword and then pay attention to the ads you’re served). Google Adwords, for example, definitely picks up text from Gmail to serve ads, as does Facebook Connect. Both have deep demographic information about us. So, instead of the Amazon/Netflix model, where recommendations are based on a single selection (e.g., users who purchased the APA Style Guide also bought a similarly boring book titled “x”) or the Stumbleupon example, where recommendations do combine variables, but still remain relatively broad (e.g., 27 year old males interested in entertainment and philosophy found these websites interesting), I’m picturing a formula like the following:
A person who (1) drives a Volkswagen + (2) owns a Mac + (3) shops at Trader Joe’s will like the following sites based on users with similar sociographic patterns. But wait, this person’s browsing behaviors indicate she spends time on teaching websites and downloads educational research articles…so, now the affinity group gets smaller and likely better defined. Combined with demographic information Google and others collect, when this person conducts a search on laptops & classrooms, the results could be better tailored to individual interest (e.g., instead of showing results for laptops for sale, Edutopia and other relevant educational sites would appear in the top 5). If a menu were provided where the user could further define their interests (Stumbleupon has the beginnings of this, but we need more than subject/lifestyle preferences), we could really select how our information is served.
[Special thanks to Christine M. for the VW example.]
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February 27th, 2010
Yesterday, I attended the RoSE Design Charrette, hosted by the Transliteracies Project at UCSB. Under Alan Liu’s guidance, many interesting, innovative interdisciplinary projects have emerged from this program.
In anticipation of attending, I read the online description of RoSE. Like seeing a movie preview, I started to imagine what I thought RoSE would be. I hoped for an improvement on current content delivery systems, in particular, I wanted something that could ease the challenge of finding high-quality information online. A couple years ago, I had a conversation with one of Transliteracies’ project members, Pablo Colapinto. As most good conversations happen, this one was over lunch and I was expressing my dissatisfaction that we weren’t living like the Jetsons yet, in particular, how they could tell their computer what they wanted to eat and it would appear on a conveyor belt.
As our conversation continued, Pablo asked me, “How would you like your information served?” This question has stuck with me, because it seems so necessary and practical, and yet doesn’t seem to be addressed by current systems. Why, when engaging in search or any use of the Internet, can’t the user filter information according to demographics or other preferences?
To a certain extent, online advertising practices provide a model for this type of targeted content delivery. Using psychographic (e.g., attitudes or opinions), sociographic (e.g., purchasing behaviors) and demographic (e.g., age, ethnicity, location) data, advertisers create profiles of specific types of users. Online user behaviors are culled by Google Analytics, Facebook Connect, and many others. Our offline data is also collected and connected to our online behaviors. Offline data aggregators include Acxiom, Experian, and TargusInfo. Combining data about what we buy with information about what websites we frequent, advertisers put us into affinity segments, which are basically groupings that reflect our behaviors and preferences. For example, someone who recently purchased a BMW and read TripAdvisor reviews for the Sofitel is likely to be put in the luxury segment. Likewise, someone who booked a Disney vacation and researched vaccination information would probably get categorized into a parenting segment. Using behavioral data of others in the same affinity segment, advertisers can predict the types of ads likely to interest you.
The problem here, of course, is that it’s creepy for our browsers or search engines to start targeting content delivery based on behaviors/preferences we’re not 100% aware are being collected. However, if so much data is being collected about me, I’d like to use it to make my life easier. There’s an obvious tension between privacy and convenience.
The process could be transparent. I could set my browser or search engine preferences to deliver information based on my preferences. Just like ordering a sandwich, where you get a list and tick the boxes you’d like, I’d like a preferences option with drop-downs where I could say how I want my information delivered. I could finally have the option of filtering for the types of websites I’ll actually read.
Given the data that Google and others collect, it seems completely possible for me to enter my age, location (although I think Google already knows this), and an interest, say ‘teaching’ into a Google search and the search engine could target its results based on what others with my similar preferences selected. The results would be similar to Amazon’s “people who purchased this book also bought…” or “people who viewed this page ended up…” In essence, I could be served the information I want using filters based on information Google already collects, and using affinity segmenting to determine what I might like based on the behaviors of others with my shared filters. I think to a degree this already happens, but is often apparent in ads/sponsored links (and the process isn’t transparent), rather than actual content filtered on projected usefulness/relevance.
These preference filters could sit on top of existing search algorithms. For example, when I search for Gran Torino, my preference filters would indicate that I’m interested in teaching, so I’d be served content based on what others interested in teaching viewed in relation to Gran Torino. Ideally, instead of getting all search results related to the film, I would receive listings targeted toward classroom use or discussions related to education. At the very least, perhaps search results could be filtered based on what others in my affinity group viewed, so it would prioritize reviews, places to purchase, etc., that I’m most likely to visit and save me a bit of sifting.
Borrowing the model of affinity marketing, by categorizing myself based on preferences, I could benefit from the collective behaviors of people with shared interests. This preference filter would make the current obtuse practice transparent and adds convenience to my search.
Joseph Turow addressed a potential downside of targeted information delivery in his lecture “When the Audience Clicks: Buying Attention in the Digital Age” presented at the Oxford Internet Institute. There’s a danger that once my Google searches and news viewing starts to be delivered according to my preferences, I limit myself to only viewing information tailored to me. Potentially, as targeting becomes more sophisticated, we may lose the breadth of information currently provided by newspapers, television, and our expansive searches, and only receive information that confirms our beliefs or supports our preferences. While to a certain extent, current media options already allow us this option (e.g., Fox News), we may further limit ourselves as content delivery becomes more targeted.
So, how to tame the super-sized information portal that is the Internet without sacrificing the breadth and choice we love? I’d like a balance between sifting through a mountain of results to find a few relevant links and restricting myself from broader views based on preferences I select. Seems possible. Perhaps we don’t need to keep the filters on all of the time, but they’d be there when we need them.
As it turns out, RoSE addresses issues of humanities scholarship, namely, identifying relationships between authors and their work. While it didn’t fulfill my Jetsons’ fantasy of chocolate cake on demand, it did prompt me to dream for a bit and envision the type of search I’d like to use.
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