Recommendation algorithm wants to show you something new

When it comes to recommendation systems, everybody's looking to increase accuracy: the Netflix Prize was awarded last July for an algorithm that improved the accuracy of the service's recommendation algorithm by 10 percent. However, computer scientists are finding a new metric to improve upon: recommendation diversity. In a paper that will be released by PNAS, a group of scientists are pushing the limits of recommendation systems, creating new algorithms that will make more tangential recommendations to users, which can help expand their interests, which will increase the longevity and utility of the recommendation system itself.

To widen the potential field of user interest, the authors developed a hybrid of two algorithms. One combined an algorithm that based its recommendations on random walks between highly connected users and material; the other mirrored the process of heat diffusion, spreading ratings at a decreasing level of potency as the recommendation had to travel further. The heat diffusion algorithm can be thought of as a system that has users connected in a network with the objects they have interacted with and evaluated, and values are passed among the items in this network to develop ratings.

The head diffusion model uses values of 1 or 0 for the material to be recommended - either a user liked something or he didn't - and takes an average of the total resources a user had assigned to an object to give the user a value. For example, if a user liked two things and disliked two others, the value assigned to the user would be one-half.

The algorithm then averaged these values for any users connected to an object, and this became the object's value in the system (for example, if two users were attached to an object and one had a value of one-half and the other had zero, the new value assigned to the object would be one quarter). All of this can be done using a small set of data, meaning the heat diffusion algorithm can make diverse yet relevant recommendations based on sparse data in one pass.

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Paper: Solving the apparent diversity-accuracy dilemma of recommender systems, Tao Zhou et al, PNAS, 2010. DOI: 10.1073/pnas.1000488107 (PNAS subscription or access purchase required)