Max's Output

Towards Stream Personalization Powered by Twitter

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Now streams have become the main source of information for many of us. Streams are central concepts in most momentous applications such as Twitter, FriendFeed, Facebook. News and blogs are also consumed as streams. Streams become so important that they even replaces search engines as a starting point of Web browsing – now a typical Web session consists in reading Twitter and Google Reader streams and following links found in these streams instead of starting with Web search. Streams are amazing because they make us involved and connected but they quickly cause information overload. You know that there should be something interesting but you cannot read it all to find that. In this post I consider how modern personalization systems address information overload problem and how it can be done in a new more social way by harnessing public micro-blogging streams such as Twitter, FriendFeed, etc.

Current practice of dealing with streams is common for all applications. The user subscribes to sources (such as news or blog feeds) or followees (e.g. in Twitter, FriendFeed) and read the stream made up of posts from the sources/followees. The problem with this approach is that there is always a compromise with the number of sources/followees that the user would like to read and the amount of information he/she is able to consume. I am sure that many of you know this problem. Even if you subscribe to, say, 30 blogs you cannot even look though all of the posts especially if there are some “top” blogs that usually issue up to 20 posts in a day. As the result you get hundreds of unread posts in just one week.

The solution to this problem is a personalized stream that contains only a moderate number of posts that are potentially interesting for the user. It sounds good but stream personalization systems do not reach wide adoption. I think that the problem of the systems is not poor algorithms they utilize but wrong assumptions they are based on. Modern personalization systems assume that the user has already formed his interests. So the system tries to identify user’s interests and then use this information to bring relevant posts via content match or collaborative filtering. The idea of building a system around user’s interests seems wrong. First of all I am sure that majority of people don’t have any formed interests. People read streams to know significant news, to identify new trends or just want to have fun. It explains why voting/commenting social news sites (e.g. Digg, Reddit) which rank news by absolute popularity without any personalization are quite popular. Even if the user has some concrete interests, posts about these interests usually make up small fraction of the user’s whole media consumption. For example, I am currently interested in semantic search because we are working on such system. But news on semantic search are quite rare and anyway I would not like to read only about semantic search stuff every day. So focusing on user’s interests is very limiting. Systems which utilize collaborative filtering try to go beyond immediate user’s interests and recommend posts that are read by other people who has similar interests. But it is not useful also as it often recommends very diverse topics and it is more about research of what people interested in particular topic also read then about what might be interesting for me. Besides being limited modern personalization systems fail to explain why they recommend this or that post to the user. It is because content match and collaborative filtering are based on statistical aggregation the result of which is hard to explain. The user have to do non-trivial interpretation and maybe even additional research to understand what recommended posts are about while the system does not provide any evidence why the user should do the effort.

So user’s interests are fluid, diverse and hard to grasp. Trying to build something around user’s interests in automatic way is in vain. To become successful personalization systems should rethink their fundamental assumption. I think that personalization system should stop trying to capture user’s interests and focus on what inspires them. The causes of user’s interests are easy to list. People usually find something interesting and worth reading in two main cases. First, it is something that is very popular, widely discussed and lead to universal resonance – trending topics. Note that it might be absolutely unrelated to the user’s interests. For example, swine flu does not touch me at all but I might be interested to know that it happens not to look like a fool among my friends who are well-informed about current global issues. Second, it is something that is inspired by people that the user knows and respects – what is hot in the user’s community. It is more likely to match immediate user’s interests but not necessarily because the user would usually like to know about all important events in his/her community. This new approach to personalization is more social than that based on user’s interests and social approaches prove to be effective for many tasks. Another advantage of the approach is that it allows explaining to the user why we recommend this post and the user should spend his/her time reading it. We need just say that too many people have posted links to it (in case of global trending topic) or that one or more influential people from the user’s community have posted/liked it.

Several years ago we cannot build a personalized user stream based on these considerations. Now, thinks to recent development and high popularity of public micro-blogging systems, we can collect enough information to identify global and in-community trending topics. Public micro-blogging systems can be used as a ranking system to identify significant posts for personalized streams. Global trending topics are already identified by a number of services (e.g. tweetmeme.com) which analyze Twitter, etc. Let me describe how I see it could work for in-community trending topics. The user’s community is defined by a list of favorite sources that the user is subscribed to or regularly visits (e.g. user’s favorite blogs, news sites and his/her friend’s feeds) and a list of followees on services like Twitter or FriendFeed. As I already mentioned above many of us cannot even look through all the posts from these sources and followees but would like not to miss important news, ideas, etc. The user’s personalized stream should include significant posts from the favorite sources and the most posted/retweeted/favorite’ed/liked links to articles/pages/posts among the user’s followees. The significant posts from the favorite sources can be identified as follows. Select those post from a source which global popularity (i.e. among all users of Twitter/FriendFeed, not only user’s followees) are greater than the average for all posts of this source. It means to select all posts from a source which causes some resonance and skip the others. As concerns the most posted/etc links among followees, they represent significant in-community trending topics. They can include links to posts from the favorite sources (for such links followees contribute to their global popularity, maybe with greater weight) or links to other sources that help to introduce the user to something new. You can find some technical details on how we identify significant posts in Twitter here.

Written by maxgrinev

May 17, 2009 at 9:26 am

Posted in Uncategorized

21 Responses

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