Friendly advice from your “Social Trust Graph”

While scanning for worthy Information Retrieval papers in the recent SIGIR 2009, I came across a paper titled “Learning to Recommend with Social Trust Ensemble“, by a team from the University of Hong Kong. This one is about recommender systems, but putting the social element into text analytics tasks is always interesting (me).

The premise is an interesting one – using your network of trust to improve classic (Collaborative Filtering) recommendations. The authors begin by observing that users’ decisions are the balance between their own tastes, and those of their trusted friends’ recommendations.

Figure 1 from "Learning to Recommend with Social Trust Ensemble" by Ma et al.

Then, they proceed to propose a model that blends analysis of classic user-item matrix where ratings of items by users are stored (the common tool of CF), with analysis of a “social trust graph” that links the user to other users, and through them to their opinions on the items.

This follows the intuition that when trying to draw a recommendation from behavior of other users (which basically is what CF does), some users’ opinions may be more important than others’, and the fact that classic CF ignores that, and treats all users as having identical importance.

The authors show results that out-perform classic CF on a dataset extracted from Epinions. That’s encouraging for any researcher interested in the contribution of the social signal into AI tasks.

free advice at renegade craft fair - CC Flickr/arimoore

However, some issues bother me with this research:

  1. Didn’t the netflix prize winning team approach (see previous post) “prove” that statistical analysis of the user-item matrix beats any external signal other teams tried to use? the answer here may be related to the sparseness of the Epinions data, which makes life very difficult for classic CF. Movie recommendations have much higher density than retail (Epinions’ domain).
  2. To evaluate, the authors sampled 80% or 90% of the ratings as training and the remaining as testing. But if you choose as training the data before the user started following someone, then test it after the user is following that someone, don’t you get a bit mixed up with cause and effect? I mean, if I follow someone and discover a product through his recommendation, there’s a high chance my opinion will also be influenced by his. So there’s no true independence between the training and test data…
  3. Eventually, the paper shows that combining two good methods (social trust graph and classic CF) outperforms each of the methods alone. The general idea of fusion or ensemble of methods is pretty much common knowledge for any Machine Learning researcher. The question should be (but it wasn’t) – does this specific ensemble of methods outperform any other ensemble? and does it fare better than the state of the art result for the same dataset?
own taste and his/her trusted friends’ favors.

The last point is of specific interest to me, having combined keyword-based retrieval with concept-based retrieval in my M.Sc. work. I could easily show that the resulting system outperformed each of the separate elements, but to counter the above questions, I further tested combining other similarly high performing methods to show performance gained there was much lower, and also showed that the combination could take a state of the art result and further improve on it.

Nevertheless, the idea of using opinions from people you know and trust (rather than authorities) in ranking recommendations is surely one that will gain more popularity, as social players start pushing ways to monetize the graphs they worked so hard to build…

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