24,966 research outputs found
Neural Collaborative Filtering
In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the
exploration of deep neural networks on recommender systems has received
relatively less scrutiny. In this work, we strive to develop techniques based
on neural networks to tackle the key problem in recommendation -- collaborative
filtering -- on the basis of implicit feedback. Although some recent work has
employed deep learning for recommendation, they primarily used it to model
auxiliary information, such as textual descriptions of items and acoustic
features of musics. When it comes to model the key factor in collaborative
filtering -- the interaction between user and item features, they still
resorted to matrix factorization and applied an inner product on the latent
features of users and items. By replacing the inner product with a neural
architecture that can learn an arbitrary function from data, we present a
general framework named NCF, short for Neural network-based Collaborative
Filtering. NCF is generic and can express and generalize matrix factorization
under its framework. To supercharge NCF modelling with non-linearities, we
propose to leverage a multi-layer perceptron to learn the user-item interaction
function. Extensive experiments on two real-world datasets show significant
improvements of our proposed NCF framework over the state-of-the-art methods.
Empirical evidence shows that using deeper layers of neural networks offers
better recommendation performance.Comment: 10 pages, 7 figure
Analysing Compression Techniques for In-Memory Collaborative Filtering
Following the recent trend of in-memory data processing, it is a usual practice to maintain collaborative filtering data in the main memory when generating recommendations in academic and industrial recommender systems.
In this paper, we study the impact of integer compression techniques for in-memory collaborative filtering data in terms of space and time efficiency. Our results provide relevant observations about when and how to compress collaborative filtering data. First, we observe that, depending on the memory constraints, compression techniques may speed up or slow down the performance of state-of-the art collaborative filtering algorithms. Second, after comparing different compression techniques, we find the Frame of Reference (FOR) technique to be the best option in terms of space and time efficiency under different memory constraints
Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lies at
the core of modern recommender systems. Ranging from early matrix factorization
to recently emerged deep learning based methods, existing efforts typically
obtain a user's (or an item's) embedding by mapping from pre-existing features
that describe the user (or the item), such as ID and attributes. We argue that
an inherent drawback of such methods is that, the collaborative signal, which
is latent in user-item interactions, is not encoded in the embedding process.
As such, the resultant embeddings may not be sufficient to capture the
collaborative filtering effect.
In this work, we propose to integrate the user-item interactions -- more
specifically the bipartite graph structure -- into the embedding process. We
develop a new recommendation framework Neural Graph Collaborative Filtering
(NGCF), which exploits the user-item graph structure by propagating embeddings
on it. This leads to the expressive modeling of high-order connectivity in
user-item graph, effectively injecting the collaborative signal into the
embedding process in an explicit manner. We conduct extensive experiments on
three public benchmarks, demonstrating significant improvements over several
state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further
analysis verifies the importance of embedding propagation for learning better
user and item representations, justifying the rationality and effectiveness of
NGCF. Codes are available at
https://github.com/xiangwang1223/neural_graph_collaborative_filtering.Comment: SIGIR 2019; the latest version of NGCF paper, which is distinct from
the version published in ACM Digital Librar
Trust based collaborative filtering
k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful
algorithm supporting recommender systems, attempts to relieve the problem
of information overload by generating predicted ratings for items users have not
expressed their opinions about; to do so, each predicted rating is computed based
on ratings given by like-minded individuals. Like-mindedness, or similarity-based
recommendation, is the cause of a variety of problems that plague recommender
systems. An alternative view of the problem, based on trust, offers the potential to
address many of the previous limiations in CF. In this work we present a varation of
kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users
to learn who and how much to trust one another by evaluating the utility of the rating
information they receive. This method redefines the way CF is performed, and
while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms
the basic similarity-based methods in terms of prediction accuracy
- …