1,726 research outputs found
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
User information needs vary significantly across different tasks, and
therefore their queries will also differ considerably in their expressiveness
and semantics. Many studies have been proposed to model such query diversity by
obtaining query types and building query-dependent ranking models. These
studies typically require either a labeled query dataset or clicks from
multiple users aggregated over the same document. These techniques, however,
are not applicable when manual query labeling is not viable, and aggregated
clicks are unavailable due to the private nature of the document collection,
e.g., in email search scenarios. In this paper, we study how to obtain query
type in an unsupervised fashion and how to incorporate this information into
query-dependent ranking models. We first develop a hierarchical clustering
algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine
query types. Then, we study three query-dependent ranking models, including two
neural models that leverage query type information as additional features, and
one novel multi-task neural model that views query type as the label for the
auxiliary query cluster prediction task. This multi-task model is trained to
simultaneously rank documents and predict query types. Our experiments on tens
of millions of real-world email search queries demonstrate that the proposed
multi-task model can significantly outperform the baseline neural ranking
models, which either do not incorporate query type information or just simply
feed query type as an additional feature.Comment: CIKM 201
Distributed Dictionary Learning
The paper studies distributed Dictionary Learning (DL) problems where the
learning task is distributed over a multi-agent network with time-varying
(nonsymmetric) connectivity. This formulation is relevant, for instance, in
big-data scenarios where massive amounts of data are collected/stored in
different spatial locations and it is unfeasible to aggregate and/or process
all the data in a fusion center, due to resource limitations, communication
overhead or privacy considerations. We develop a general distributed
algorithmic framework for the (nonconvex) DL problem and establish its
asymptotic convergence. The new method hinges on Successive Convex
Approximation (SCA) techniques coupled with i) a gradient tracking mechanism
instrumental to locally estimate the missing global information; and ii) a
consensus step, as a mechanism to distribute the computations among the agents.
To the best of our knowledge, this is the first distributed algorithm with
provable convergence for the DL problem and, more in general, bi-convex
optimization problems over (time-varying) directed graphs
- …