Improving Cold-Start Recommendation with a Cross-Site User Interest Transfer Model
Date Issued
2015
Date
2015
Author(s)
Huang, Yu-Yang
Abstract
In this work, we attempt to transfer user interests across websites for cold-start recommendation. Both rating-based and text-based recommender systems may suffer from the cold-start problem. One effective way to ease the cold-start problem is to introduce auxiliary data. Users nowadays hold multiple accounts across websites. If data can be obtained via the account linking mechanism, there will be an abundant supply of auxiliary data. Although this cross-site approach can be exploited to solve the cold-start problem, it is often the case that we have to deal with heterogeneous data when transferring knowledge across websites. In this work, we make use of unstructured auxiliary text to solve the cold-start problem. In particular, we extract topic vectors from source-domain text, and use the similarity scores between users to construct ""nearest-neighbor pseudo data"", a set of weighted (pseudo) samples which can be used to estimate the unknown parameters of the distribution over the user latent factors in the target domain. The inference process and model structure of the probabilistic matrix factorization has been modified to utilize this pseudo dataset. Improvement over previous methods, especially for the cold-start users, has been demonstrated with experiments on a real-world cross-website dataset.
Subjects
Cold-start problem
Collaborative filtering
Transfer learning
Neighborhood methods
Matrix factorization
Type
thesis
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