3,416 research outputs found
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
Collaborative Research on Academic History using Linked Open Data: A Proposal for the Heloise Common Research Model
International audienceThe paper presents a proposal for the Heloise Common Research Model (HCRM), to be implemented for the European research network on digital academic history – Heloise. The objective of Heloise is to interlink databases and other digital resources stemming from several research projects in the field of academic history, to provide an integrated database for federated research on the network databases. The HCRM defines three layers: the Repository Layer, the Application Layer and the Research Interface Layer, which are presented in detail. As part of the application and research interface layer, essential concepts are the symogih.org ontology and a Heloise network-specific thesaurus. The concepts have been tested on a sample of Heloise network’s datasets as a part of a prototype of the envisaged platform that the authors have started implementing. The paper concludes with future developments to be accomplished within the Heloise network
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
Delivery of 360° videos in edge caching assisted wireless cellular networks
In recent years, 360° videos have become increasingly popular on commercial social platforms, and are a vital part of emerging Virtual Reality (VR) applications. However, the delivery of 360° videos requires significant bandwidth resources, which makes streaming of such data on mobile networks challenging. The bandwidth required for delivering 360° videos can be reduced by exploiting the fact that users are interested in viewing only a part of the video scene, the requested viewport. As different users may request different viewports, some parts of the 360° scenes may be more popular than others. 360° video delivery on mobile networks can be facilitated by caching popular content at edge servers, and delivering it from there to the users. However, existing edge caching schemes do not take full potential of the unequal popularity of different parts of a video, which renders them inefficient for caching 360° videos. Inspired by the above, in this thesis, we investigate how advanced 360° video coding tools, i.e., encoding into multiple quality layers and tiles, can be utilized to build more efficient wireless edge caching schemes for 360° videos. The above encoding allows the caching of only the parts of the 360° videos that are popular in high quality. To understand how edge caching schemes can benefit from 360° video coding, we compare the caching of 360° videos encoded into multiple quality layers and tiles with layer-agnostic and tile-agnostic schemes. To cope with the fact that the content popularity distribution may be unknown, we use machine learning techniques, for both Video on Demand (VoD), and live streaming scenarios. From our findings, it is clear that by taking into account the aforementioned 360° video characteristics leads to an increased performance in terms of the quality of the video delivered to the users, and the usage of the backhaul links
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