5,232 research outputs found
Learning Continuous User Representations through Hybrid Filtering with doc2vec
Players in the online ad ecosystem are struggling to acquire the user data
required for precise targeting. Audience look-alike modeling has the potential
to alleviate this issue, but models' performance strongly depends on quantity
and quality of available data. In order to maximize the predictive performance
of our look-alike modeling algorithms, we propose two novel hybrid filtering
techniques that utilize the recent neural probabilistic language model
algorithm doc2vec. We apply these methods to data from a large mobile ad
exchange and additional app metadata acquired from the Apple App store and
Google Play store. First, we model mobile app users through their app usage
histories and app descriptions (user2vec). Second, we introduce context
awareness to that model by incorporating additional user and app-related
metadata in model training (context2vec). Our findings are threefold: (1) the
quality of recommendations provided by user2vec is notably higher than current
state-of-the-art techniques. (2) User representations generated through hybrid
filtering using doc2vec prove to be highly valuable features in supervised
machine learning models for look-alike modeling. This represents the first
application of hybrid filtering user models using neural probabilistic language
models, specifically doc2vec, in look-alike modeling. (3) Incorporating context
metadata in the doc2vec model training process to introduce context awareness
has positive effects on performance and is superior to directly including the
data as features in the downstream supervised models.Comment: 10 page
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
Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in
recommender systems in industry, owing to its strength in user interest
modeling and ease in online personalization. By constructing a user's profile
with the items that the user has consumed, ICF recommends items that are
similar to the user's profile. With the prevalence of machine learning in
recent years, significant processes have been made for ICF by learning item
similarity (or representation) from data. Nevertheless, we argue that most
existing works have only considered linear and shallow relationship between
items, which are insufficient to capture the complicated decision-making
process of users.
In this work, we propose a more expressive ICF solution by accounting for the
nonlinear and higher-order relationship among items. Going beyond modeling only
the second-order interaction (e.g. similarity) between two items, we
additionally consider the interaction among all interacted item pairs by using
nonlinear neural networks. Through this way, we can effectively model the
higher-order relationship among items, capturing more complicated effects in
user decision-making. For example, it can differentiate which historical
itemsets in a user's profile are more important in affecting the user to make a
purchase decision on an item. We treat this solution as a deep variant of ICF,
thus term it as DeepICF. To justify our proposal, we perform empirical studies
on two public datasets from MovieLens and Pinterest. Extensive experiments
verify the highly positive effect of higher-order item interaction modeling
with nonlinear neural networks. Moreover, we demonstrate that by more
fine-grained second-order interaction modeling with attention network, the
performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI
Mobile Multimedia Recommendation in Smart Communities: A Survey
Due to the rapid growth of internet broadband access and proliferation of
modern mobile devices, various types of multimedia (e.g. text, images, audios
and videos) have become ubiquitously available anytime. Mobile device users
usually store and use multimedia contents based on their personal interests and
preferences. Mobile device challenges such as storage limitation have however
introduced the problem of mobile multimedia overload to users. In order to
tackle this problem, researchers have developed various techniques that
recommend multimedia for mobile users. In this survey paper, we examine the
importance of mobile multimedia recommendation systems from the perspective of
three smart communities, namely, mobile social learning, mobile event guide and
context-aware services. A cautious analysis of existing research reveals that
the implementation of proactive, sensor-based and hybrid recommender systems
can improve mobile multimedia recommendations. Nevertheless, there are still
challenges and open issues such as the incorporation of context and social
properties, which need to be tackled in order to generate accurate and
trustworthy mobile multimedia recommendations
Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback
Recommender systems (RSs) provide an effective way of alleviating the
information overload problem by selecting personalized items for different
users. Latent factors based collaborative filtering (CF) has become the popular
approaches for RSs due to its accuracy and scalability. Recently, online social
networks and user-generated content provide diverse sources for recommendation
beyond ratings. Although {\em social matrix factorization} (Social MF) and {\em
topic matrix factorization} (Topic MF) successfully exploit social relations
and item reviews, respectively, both of them ignore some useful information. In
this paper, we investigate the effective data fusion by combining the
aforementioned approaches. First, we propose a novel model {\em \mbox{MR3}} to
jointly model three sources of information (i.e., ratings, item reviews, and
social relations) effectively for rating prediction by aligning the latent
factors and hidden topics. Second, we incorporate the implicit feedback from
ratings into the proposed model to enhance its capability and to demonstrate
its flexibility. We achieve more accurate rating prediction on real-life
datasets over various state-of-the-art methods. Furthermore, we measure the
contribution from each of the three data sources and the impact of implicit
feedback from ratings, followed by the sensitivity analysis of hyperparameters.
Empirical studies demonstrate the effectiveness and efficacy of our proposed
model and its extension.Comment: 27 pages, 11 figures, 6 tables, ACM TKDD 201
Attributes Coupling based Item Enhanced Matrix Factorization Technique for Recommender Systems
Recommender system has attracted lots of attentions since it helps users
alleviate the information overload problem. Matrix factorization technique is
one of the most widely employed collaborative filtering techniques in the
research of recommender systems due to its effectiveness and efficiency in
dealing with very large user-item rating matrices. Recently, based on the
intuition that additional information provides useful insights for matrix
factorization techniques, several recommendation algorithms have utilized
additional information to improve the performance of matrix factorization
methods. However, the majority focus on dealing with the cold start user
problem and ignore the cold start item problem. In addition, there are few
suitable similarity measures for these content enhanced matrix factorization
approaches to compute the similarity between categorical items. In this paper,
we propose attributes coupling based item enhanced matrix factorization method
by incorporating item attribute information into matrix factorization technique
as well as adapting the coupled object similarity to capture the relationship
between items. Item attribute information is formed as an item relationship
regularization term to regularize the process of matrix factorization.
Specifically, the similarity between items is measured by the Coupled Object
Similarity considering coupling between items. Experimental results on two real
data sets show that our proposed method outperforms state-of-the-art
recommendation algorithms and can effectively cope with the cold start item
problem when more item attribute information is available.Comment: 15 page
Collaborative filtering via sparse Markov random fields
Recommender systems play a central role in providing individualized access to
information and services. This paper focuses on collaborative filtering, an
approach that exploits the shared structure among mind-liked users and similar
items. In particular, we focus on a formal probabilistic framework known as
Markov random fields (MRF). We address the open problem of structure learning
and introduce a sparsity-inducing algorithm to automatically estimate the
interaction structures between users and between items. Item-item and user-user
correlation networks are obtained as a by-product. Large-scale experiments on
movie recommendation and date matching datasets demonstrate the power of the
proposed method
Personalized QoS Prediction of Cloud Services via Learning Neighborhood-based Model
The explosion of cloud services on the Internet brings new challenges in
service discovery and selection. Particularly, the demand for efficient
quality-of-service (QoS) evaluation is becoming urgently strong. To address
this issue, this paper proposes neighborhood-based approach for QoS prediction
of cloud services by taking advantages of collaborative intelligence. Different
from heuristic collaborative filtering and matrix factorization, we define a
formal neighborhood-based prediction framework which allows an efficient global
optimization scheme, and then exploit different baseline estimate component to
improve predictive performance. To validate the proposed methods, a large-scale
QoS-specific dataset which consists of invocation records from 339 service
users on 5,825 web services on a world-scale distributed network is used.
Experimental results demonstrate that the learned neighborhood-based models can
overcome existing difficulties of heuristic collaborative filtering methods and
achieve superior performance than state-of-the-art prediction methods
Neural Tensor Factorization
Neural collaborative filtering (NCF) and recurrent recommender systems (RRN)
have been successful in modeling user-item relational data. However, they are
also limited in their assumption of static or sequential modeling of relational
data as they do not account for evolving users' preference over time as well as
changes in the underlying factors that drive the change in user-item
relationship over time. We address these limitations by proposing a Neural
Tensor Factorization (NTF) model for predictive tasks on dynamic relational
data. The NTF model generalizes conventional tensor factorization from two
perspectives: First, it leverages the long short-term memory architecture to
characterize the multi-dimensional temporal interactions on relational data.
Second, it incorporates the multi-layer perceptron structure for learning the
non-linearities between different latent factors. Our extensive experiments
demonstrate the significant improvement in rating prediction and link
prediction on dynamic relational data by our NTF model over both neural network
based factorization models and other traditional methods.Comment: 9 pages. Submitted to KD
Using Temporal Data for Making Recommendations
We treat collaborative filtering as a univariate time series estimation
problem: given a user's previous votes, predict the next vote. We describe two
families of methods for transforming data to encode time order in ways amenable
to off-the-shelf classification and density estimation tools, and examine the
results of using these approaches on several real-world data sets. The
improvements in predictive accuracy we realize recommend the use of other
predictive algorithms that exploit the temporal order of data.Comment: Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001
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