AI News, Artificial Intelligence/Search/Recommender Systems/Boltzmann Machines

Artificial Intelligence/Search/Recommender Systems/Boltzmann Machines

This article describes using Boltzmann Machines for recommender systems.

Our task is to predict, as precise as possible, what grade a user would give to a new item, based on his and other user's previous ratings, in order to recommend items which he or she may like.

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For example, when recommending movies, some feature may be highly positive for romantic movies, zero or negative for movies without any romantic appeal, and highly negative for cynical movies which ridicule romantics.

The corresponding feature in user feature vectors will be negative for users who hate romantics, and positive in opposite case.

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{\displaystyle E={1 \over 2}\sum _{(u,i,g)\in T}(\mathbf {x} _{u}\mathbf {y} _{i}-g)^{2}}

However, for users and movies with few data, this approach tends to construct huge feature vectors, because this fits the data better.

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