940 research outputs found
Content-aware Neural Hashing for Cold-start Recommendation
Content-aware recommendation approaches are essential for providing
meaningful recommendations for \textit{new} (i.e., \textit{cold-start}) items
in a recommender system. We present a content-aware neural hashing-based
collaborative filtering approach (NeuHash-CF), which generates binary hash
codes for users and items, such that the highly efficient Hamming distance can
be used for estimating user-item relevance. NeuHash-CF is modelled as an
autoencoder architecture, consisting of two joint hashing components for
generating user and item hash codes. Inspired from semantic hashing, the item
hashing component generates a hash code directly from an item's content
information (i.e., it generates cold-start and seen item hash codes in the same
manner). This contrasts existing state-of-the-art models, which treat the two
item cases separately. The user hash codes are generated directly based on user
id, through learning a user embedding matrix. We show experimentally that
NeuHash-CF significantly outperforms state-of-the-art baselines by up to 12\%
NDCG and 13\% MRR in cold-start recommendation settings, and up to 4\% in both
NDCG and MRR in standard settings where all items are present while training.
Our approach uses 2-4x shorter hash codes, while obtaining the same or better
performance compared to the state of the art, thus consequently also enabling a
notable storage reduction.Comment: Accepted to SIGIR 202
Discrete Factorization Machines for Fast Feature-based Recommendation
User and item features of side information are crucial for accurate
recommendation. However, the large number of feature dimensions, e.g., usually
larger than 10^7, results in expensive storage and computational cost. This
prohibits fast recommendation especially on mobile applications where the
computational resource is very limited. In this paper, we develop a generic
feature-based recommendation model, called Discrete Factorization Machine
(DFM), for fast and accurate recommendation. DFM binarizes the real-valued
model parameters (e.g., float32) of every feature embedding into binary codes
(e.g., boolean), and thus supports efficient storage and fast user-item score
computation. To avoid the severe quantization loss of the binarization, we
propose a convergent updating rule that resolves the challenging discrete
optimization of DFM. Through extensive experiments on two real-world datasets,
we show that 1) DFM consistently outperforms state-of-the-art binarized
recommendation models, and 2) DFM shows very competitive performance compared
to its real-valued version (FM), demonstrating the minimized quantization loss.
This work is accepted by IJCAI 2018.Comment: Appeared in IJCAI 201
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network
platforms nowadays, aiming to maximize user satisfaction along with other key
business objectives. Twitter is no exception. Despite the fact that Twitter
data has been extensively used to understand socioeconomic and political
phenomena and user behaviour, the implicit feedback provided by users on Tweets
through their engagements on the Home Timeline has only been explored to a
limited extent. At the same time, there is a lack of large-scale public social
network datasets that would enable the scientific community to both benchmark
and build more powerful and comprehensive models that tailor content to user
interests. By releasing an original dataset of 160 million Tweets along with
engagement information, Twitter aims to address exactly that. During this
release, special attention is drawn on maintaining compliance with existing
privacy laws. Apart from user privacy, this paper touches on the key challenges
faced by researchers and professionals striving to predict user engagements. It
further describes the key aspects of the RecSys 2020 Challenge that was
organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table
- …