30,147 research outputs found
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
A large amount of information exists in reviews written by users. This source
of information has been ignored by most of the current recommender systems
while it can potentially alleviate the sparsity problem and improve the quality
of recommendations. In this paper, we present a deep model to learn item
properties and user behaviors jointly from review text. The proposed model,
named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel
neural networks coupled in the last layers. One of the networks focuses on
learning user behaviors exploiting reviews written by the user, and the other
one learns item properties from the reviews written for the item. A shared
layer is introduced on the top to couple these two networks together. The
shared layer enables latent factors learned for users and items to interact
with each other in a manner similar to factorization machine techniques.
Experimental results demonstrate that DeepCoNN significantly outperforms all
baseline recommender systems on a variety of datasets.Comment: WSDM 201
Methods to integrate a language model with semantic information for a word prediction component
Most current word prediction systems make use of n-gram language models (LM)
to estimate the probability of the following word in a phrase. In the past
years there have been many attempts to enrich such language models with further
syntactic or semantic information. We want to explore the predictive powers of
Latent Semantic Analysis (LSA), a method that has been shown to provide
reliable information on long-distance semantic dependencies between words in a
context. We present and evaluate here several methods that integrate LSA-based
information with a standard language model: a semantic cache, partial
reranking, and different forms of interpolation. We found that all methods show
significant improvements, compared to the 4-gram baseline, and most of them to
a simple cache model as well.Comment: 10 pages ; EMNLP'2007 Conference (Prague
Neural Networks for Information Retrieval
Machine learning plays a role in many aspects of modern IR systems, and deep
learning is applied in all of them. The fast pace of modern-day research has
given rise to many different approaches for many different IR problems. The
amount of information available can be overwhelming both for junior students
and for experienced researchers looking for new research topics and directions.
Additionally, it is interesting to see what key insights into IR problems the
new technologies are able to give us. The aim of this full-day tutorial is to
give a clear overview of current tried-and-trusted neural methods in IR and how
they benefit IR research. It covers key architectures, as well as the most
promising future directions.Comment: Overview of full-day tutorial at SIGIR 201
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