2,158 research outputs found
LRMM: Learning to Recommend with Missing Modalities
Multimodal learning has shown promising performance in content-based
recommendation due to the auxiliary user and item information of multiple
modalities such as text and images. However, the problem of incomplete and
missing modality is rarely explored and most existing methods fail in learning
a recommendation model with missing or corrupted modalities. In this paper, we
propose LRMM, a novel framework that mitigates not only the problem of missing
modalities but also more generally the cold-start problem of recommender
systems. We propose modality dropout (m-drop) and a multimodal sequential
autoencoder (m-auto) to learn multimodal representations for complementing and
imputing missing modalities. Extensive experiments on real-world Amazon data
show that LRMM achieves state-of-the-art performance on rating prediction
tasks. More importantly, LRMM is more robust to previous methods in alleviating
data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201
Context-Aware Systems for Sequential Item Recommendation
Quizlet is the most popular online learning tool in the United States, and is
used by over 2/3 of high school students, and 1/2 of college students. With
more than 95% of Quizlet users reporting improved grades as a result, the
platform has become the de-facto tool used in millions of classrooms. In this
paper, we explore the task of recommending suitable content for a student to
study, given their prior interests, as well as what their peers are studying.
We propose a novel approach, i.e. Neural Educational Recommendation Engine
(NERE), to recommend educational content by leveraging student behaviors rather
than ratings. We have found that this approach better captures social factors
that are more aligned with learning. NERE is based on a recurrent neural
network that includes collaborative and content-based approaches for
recommendation, and takes into account any particular student's speed, mastery,
and experience to recommend the appropriate task. We train NERE by jointly
learning the user embeddings and content embeddings, and attempt to predict the
content embedding for the final timestamp. We also develop a confidence
estimator for our neural network, which is a crucial requirement for
productionizing this model. We apply NERE to Quizlet's proprietary dataset, and
present our results. We achieved an R^2 score of 0.81 in the content embedding
space, and a recall score of 54% on our 100 nearest neighbors. This vastly
exceeds the recall@100 score of 12% that a standard matrix-factorization
approach provides. We conclude with a discussion on how NERE will be deployed,
and position our work as one of the first educational recommender systems for
the K-12 space
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
Data Sets: Word Embeddings Learned from Tweets and General Data
A word embedding is a low-dimensional, dense and real- valued vector
representation of a word. Word embeddings have been used in many NLP tasks.
They are usually gener- ated from a large text corpus. The embedding of a word
cap- tures both its syntactic and semantic aspects. Tweets are short, noisy and
have unique lexical and semantic features that are different from other types
of text. Therefore, it is necessary to have word embeddings learned
specifically from tweets. In this paper, we present ten word embedding data
sets. In addition to the data sets learned from just tweet data, we also built
embedding sets from the general data and the combination of tweets with the
general data. The general data consist of news articles, Wikipedia data and
other web data. These ten embedding models were learned from about 400 million
tweets and 7 billion words from the general text. In this paper, we also
present two experiments demonstrating how to use the data sets in some NLP
tasks, such as tweet sentiment analysis and tweet topic classification tasks
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