The task of matching data from two heterogeneous domains naturally arises in various areas such
\nas web search, collaborative filtering, and drug design. In web search, existing work has designed
\nrelevance models to match queries and documents by exploiting either user clicks or content of
\nqueries and documents. To the best of our knowledge, however, there has been little work on principled
\napproaches to leveraging both clicks and content to learn a matching model for search. In
\nthis paper, we propose a framework for learning to match heterogeneous objects. The framework
\nlearns two linear mappings for two objects respectively, and matches them via the dot product of
\ntheir images after mapping. Moreover, when different regularizations are enforced, the framework
\nrenders a rich family of matching models. With orthonormal constraints on mapping functions,
\nthe framework subsumes Partial Least Squares (PLS) as a special case. Alternatively, with a \u21131+\u21132
\nregularization, we obtain a new model called Regularized Mapping to Latent Structures (RMLS).
\nRMLS enjoys many advantages over PLS, including lower time complexity and easy parallelization.
\nTo further understand the matching framework, we conduct generalization analysis and apply
\nthe result to both PLS and RMLS. We apply the framework to web search and implement both PLS
\nand RMLS using a click-through bipartite with metadata representing features of queries and documents.
\nWe test the efficacy and scalability of RMLS and PLS on large scale web search problems.
\nThe results show that both PLS and RMLS can significantly outperform baseline methods, while
\nRMLS substantially speeds up the learning process.<\/p>\n","protected":false},"excerpt":{"rendered":"
The task of matching data from two heterogeneous domains naturally arises in various areas such as web search, collaborative filtering, and drug design. In web search, existing work has designed relevance models to match queries and documents by exploiting either user clicks or content of queries and documents. 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