“Yahoo will not use Bing. Bing is a branded search engine that Microsoft is building on top of its search back-end and we will build our own search front-end on that same Microsoft back-end. It (using Bing) is not the case, at least as envisioned at the moment”

This actually makes perfect sense. Stop spending tons of resources on crawling and ranking in a futile war with Google, and focus on building the user experience over it, leveraging Yahoo’s advantage – content. Raghavan mentions scenarios that sound a lot like Yahoo shortcuts (that’s really old news) as one example of how to deliver a more complete experience over commodity search results.

The article then goes on to discuss the second focus for Yahoo, social applications, and mentions Microsoft’s tie-up with Facebook for access to social graph. Raghavan is quoted as saying:

“Social networks are not just a place to hang out, but to get things done. It predates the web.. I’m not sure where the sweet spot is, we’re still doing research on it”

Also makes perfect sense. With Google as a common enemy, and Microsoft a Facebook partner, Yahoo may be better positioned to deliver social applications that leverage the de-facto standard of Facebook graph, rather than push its own failed networks.

So why is my post title suggesting what it’s suggesting??

There is one catch in sub-contracting your search results: you are now limited with what you can do in search ranking. The best you can do is re-rank the set of results Microsoft’s technology supplied you with before presenting it to the user. As I’ve pointed out in the past when talking about Delver’s technology, social (graph-based) search is a game that cannot be played by reranking, since it’s a classic long tail problem. So when you can’t interfere with how search results are ranked, you also can’t deliver true social search, as Google recently did. One less social application Yahoo can build…

Mor Naaman of Rutgers University and formerly at Yahoo! Research gave an excellent talk on using social inputs to improve the experience of multimedia search. The general theme was about discovering metadata for a given multimedia concept from web 2.0 sites, then using those to cluster potential results and choose representative ones.

In one application, this approach was used to identify “representative” photos of a certain landmark, say the Golden Gate bridge, see WorldExplorer for an illustration. So first, you’d find all flickr photos geotagged and/or fickr-tagged by the location and name of the bridge (or any given landmark). Next, image processing (SIFT)  is applied to those images to cluster them into subsets that are likely to be of the same section and/or perspective of the bridge. Finally, relations between the images in each cluster are formed based on the visual relation, and link analysis is employed to find a “canonical view”. The result is what we see on the right sidebar in World Explorer, and described in this WWW’08 paper.

[Update: Mor commented that the content-based analysis part is not yet deployed in World Explorer. Thanks Mor!]

Another example applied this approach to concerts on YouTube, and the purpose was to find good clips of the concert itself, rather than videos discussing it etc. Metadata describing the event (say, an Iron Maiden concert) was collected from both YouTube and sites such as Upcoming.org, and Audio Fingerprinting was employed to detect overlapping video sections, as it’s quite likely the concert itself would have the most overlap. Note that in both cases, the image/audio processing is a heavy task, and applying it only to a small subset filtered by social tags makes the work involved more feasible.

I’ll talk about the keynote (by Prof. Ben Schneiderman) on another post, this one is already way too long… Here are soundbites from some other talks:

Emil Ismalon of Collarity referred to personalized search (e.g. Google’s) as a form of overfitting, not letting me learn anything new as it trains itself only on my own history. That, of course, as a motivation for community-based personalization. 

Ido Guy of IBM talked about research they did comparing social network extracted from public and private sources. The bottom line is that some forms of social relations are stronger, representing collaboration (working on projects together, co-authoring papers or patents), and others are weaker, being more around the socializing activities (friending/following on SN, commenting on blogs etc) . Of course, that would be relevant for Enterprise social graph, not necessarily personal life…

Daphne Raban of Haifa University summarized her (empirical) research into motivations of participants in Q&A sites. The main bottom lines were: 1) money was less important to people who participate very often, but it’s a catalyst, 2) Being awarded with gratitude and conversation is the main factor driving people to become more frequent participants, and 3) in quality comparison, paid results ranked highest, free community results (Yahoo! Answers) ranked close, and unpaid single experts ranked lowest.

MovieCritic is dead for a few years now, but recommender systems are now everywhere. NetFlix runs one of the most successful commercial implementations (Amazon another classic example, “People who bought this book…”), and two years ago they challenged researches to come up with a system that would perform 10% better than their own, in predicting users’ ratings. The best achieving team so far almost got there, and today I attended a talk in the Technion by Yehuda Koren, one of the team members and a researcher at Yahoo! Research Haifa lab.

Most methods follow the neighborhood-based model – find an item’s neighbours (in some representation), and predict based on their rating. This may be done in a user-user matching (find users like this user, then check their rating) or item-item (find items like the rated item, then predict based on how the user rated those items). One of the interesting approaches proposed by Koren’s team represented both users and movies in the same space, then looked for similarity in this unified space.

The most striking finding for me, however, was that winning strategies did not use anything from the movie’s “content” features. Genre, director, actors, length, etc. – all these did not produce any additional value beyond the plain statistical analysis and correlation of ratings and users, and are therefore not used at all. In fact, Koren claims that knowing that a certain user is a Tom Hanks fan makes no difference, we will infer this from the recommendations anyway (assuming there are enough of them of course).

I find that almost sad… Not being able to intelligently reason over the underlying logic exposed by an AI software is a tremendous drawback in my eyes, even if the overall prediction score is better. Telling the user “you may want to watch this movie because A and B and C” can benefit in more satisfaction by the user, understanding even the incorrect predictions, and possibly leading to a feedback cycle. Doing away with it is like showing web search results without keyword highlighting, no visible cue for the user why this result was returned (“…trust me, I know what’s the right answer for you!“).