2,612 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Reply With: Proactive Recommendation of Email Attachments
Email responses often contain items-such as a file or a hyperlink to an
external document-that are attached to or included inline in the body of the
message. Analysis of an enterprise email corpus reveals that 35% of the time
when users include these items as part of their response, the attachable item
is already present in their inbox or sent folder. A modern email client can
proactively retrieve relevant attachable items from the user's past emails
based on the context of the current conversation, and recommend them for
inclusion, to reduce the time and effort involved in composing the response. In
this paper, we propose a weakly supervised learning framework for recommending
attachable items to the user. As email search systems are commonly available,
we constrain the recommendation task to formulating effective search queries
from the context of the conversations. The query is submitted to an existing IR
system to retrieve relevant items for attachment. We also present a novel
strategy for generating labels from an email corpus---without the need for
manual annotations---that can be used to train and evaluate the query
formulation model. In addition, we describe a deep convolutional neural network
that demonstrates satisfactory performance on this query formulation task when
evaluated on the publicly available Avocado dataset and a proprietary dataset
of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on
Information and Knowledge Management. 201
Disentangled Graph Social Recommendation
Social recommender systems have drawn a lot of attention in many online web
services, because of the incorporation of social information between users in
improving recommendation results. Despite the significant progress made by
existing solutions, we argue that current methods fall short in two
limitations: (1) Existing social-aware recommendation models only consider
collaborative similarity between items, how to incorporate item-wise semantic
relatedness is less explored in current recommendation paradigms. (2) Current
social recommender systems neglect the entanglement of the latent factors over
heterogeneous relations (e.g., social connections, user-item interactions).
Learning the disentangled representations with relation heterogeneity poses
great challenge for social recommendation. In this work, we design a
Disentangled Graph Neural Network (DGNN) with the integration of latent memory
units, which empowers DGNN to maintain factorized representations for
heterogeneous types of user and item connections. Additionally, we devise new
memory-augmented message propagation and aggregation schemes under the graph
neural architecture, allowing us to recursively distill semantic relatedness
into the representations of users and items in a fully automatic manner.
Extensive experiments on three benchmark datasets verify the effectiveness of
our model by achieving great improvement over state-of-the-art recommendation
techniques. The source code is publicly available at:
https://github.com/HKUDS/DGNN.Comment: Accepted by IEEE ICDE 202
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