Yes, two consecutive posts on the same annual event are not a good sign to my virtual activity level… point taken.
So 2 weeks ago, Microsoft Israel held its second ReCon conference on Recommendations and Personalization, turning its fine 2014 start into a tradition worth waiting for. This time it was more condensed than last year (good move!) and just as interesting. So here are three highlights I found worth reporting about:
Uri Barash of the hosting team gave the first keynote on Cortana integration in Windows 10, talking about the challenges and principles used. Microsoft places a high empasis on the user’s trust, hence Cortana does not use any interests that are not explicitly written in Cortana’s notebook, validated by the user. If indeed correct, that’s somewhat surprising, as it limits the recommendation quality and moreover – the discovery experience for the user, picking up potential interests from the user’s activity. I’d still presume that all these implicit interests are probably used behind the scenes, to optimize the content from explicit interests.
IBM Haifa Research Labs have been doing work for some years now on enterprise social networks, and mining connections and knowledge from such networks. In ReCon this year, Roy Levin presented a paper to be published in SIGIR’15, titled “Islands in the Stream: A Study of Item Recommendation within an Enterprise Social Stream“. In the paper, they discuss a feature for a personalized newsfeed included in IBM’s enterprise social network “IBM Connections”, and provide some background and the personalized ranking logic for the feed items.
They then move on to describe a survey they have made among users of the product, to analyze their opinions on specific items recommended for them in their newsfeed, similar to Facebook’s newsfeed surveys. Through these surveys, the IBM researchers attempted to identify correlations between various feed item factors, such as post and author popularity, post personalization score, how surprising an item may be to a user and how likely a user is to want such serevdipity, etc. The actual findings are in the paper, but what may actually be even more interesting is the deep dissection in the paper of the internal workings of the ranking model.
Another interesting talk was by Roy Sasson, Chief Data Scientist at Outbrain. Roy delivered a fascinating talk about learning from lack of signals. He began with an outline of general measurement pitfalls, demonstrating them on Outbrain widgets when analyzing low numbers of of clicks on recommended items. Was the widget visible to the user? where was it positioned in the page (areas of blindness)? what items were next to the analyzed item? were they clicked? and so on.
Roy then proceeded to talk about what we may actually be able to learn from lack of sharing to social networks. We all know that content that gets shared a lot on social networks is considered viral, driving a lot of discussion and engagement. But what about content that gets practically no sharing at all? and more precisely, what kind of content gets a lot of views, but no sharing? Well, if you hadn’t guessed already, that will likely be content users are very interested to see, but would not admit to it, namely provocative and adult material. So in a way, leveraging this reverse correlation helped Outbrain automatically identify porn and other sensitive material. This was then not used to filter all of this content out – after all, users do want to view it… but it was used to make sure that the recommendation strip includes only 1-2 such items so they don’t take over the widget, making it seem like this is all Outbrain has to offer. Smart use of data indeed.
Elad Yom-Tov from Microsoft Research described work his team did on enhancing Collaborative Filtering using browse logs. They experimented with adding user browser logs (visited urls) and search queries to the CF matrix in various ways to help bootstrapping users with little data and to better identify short-term (recent) intent for these users.
The second keynote was by Ronny Lempel of Yahoo! Labs in Haifa. Ronny talked about multi-user devices, in particular smart TVs, and how recommendations should take into account the user that is currently in front of the device (although this information is not readily available). The heuristic his team used was that the audience usually doesn’t change in consecutive programs watched, and so using the last program as context to recommending the next program will help model that unknown audience.
The Alter Egozi 