3,726 research outputs found
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
Recommender systems help users deal with information overload by providing
tailored item suggestions to them. The recommendation of news is often
considered to be challenging, since the relevance of an article for a user can
depend on a variety of factors, including the user's short-term reading
interests, the reader's context, or the recency or popularity of an article.
Previous work has shown that the use of Recurrent Neural Networks is promising
for the next-in-session prediction task, but has certain limitations when only
recorded item click sequences are used as input. In this work, we present a
contextual hybrid, deep learning based approach for session-based news
recommendation that is able to leverage a variety of information types. We
evaluated our approach on two public datasets, using a temporal evaluation
protocol that simulates the dynamics of a news portal in a realistic way. Our
results confirm the benefits of considering additional types of information,
including article popularity and recency, in the proposed way, resulting in
significantly higher recommendation accuracy and catalog coverage than other
session-based algorithms. Additional experiments show that the proposed
parameterizable loss function used in our method also allows us to balance two
usually conflicting quality factors, accuracy and novelty.
Keywords: Artificial Neural Networks, Context-Aware Recommender Systems,
Hybrid Recommender Systems, News Recommender Systems, Session-based
RecommendationComment: 20 pgs. Published at IEEE Access, Volume 7, 2019.
https://ieeexplore.ieee.org/document/890868
A Neural Network Approach to Joint Modeling Social Networks and Mobile Trajectories
The accelerated growth of mobile trajectories in location-based services
brings valuable data resources to understand users' moving behaviors. Apart
from recording the trajectory data, another major characteristic of these
location-based services is that they also allow the users to connect whomever
they like. A combination of social networking and location-based services is
called as location-based social networks (LBSN). As shown in previous works,
locations that are frequently visited by socially-related persons tend to be
correlated, which indicates the close association between social connections
and trajectory behaviors of users in LBSNs. In order to better analyze and mine
LBSN data, we present a novel neural network model which can joint model both
social networks and mobile trajectories. In specific, our model consists of two
components: the construction of social networks and the generation of mobile
trajectories. We first adopt a network embedding method for the construction of
social networks: a networking representation can be derived for a user. The key
of our model lies in the component of generating mobile trajectories. We have
considered four factors that influence the generation process of mobile
trajectories, namely user visit preference, influence of friends, short-term
sequential contexts and long-term sequential contexts. To characterize the last
two contexts, we employ the RNN and GRU models to capture the sequential
relatedness in mobile trajectories at different levels, i.e., short term or
long term. Finally, the two components are tied by sharing the user network
representations. Experimental results on two important applications demonstrate
the effectiveness of our model. Especially, the improvement over baselines is
more significant when either network structure or trajectory data is sparse.Comment: Accepted by ACM TOI
A Probabilistic Model for the Cold-Start Problem in Rating Prediction using Click Data
One of the most efficient methods in collaborative filtering is matrix
factorization, which finds the latent vector representations of users and items
based on the ratings of users to items. However, a matrix factorization based
algorithm suffers from the cold-start problem: it cannot find latent vectors
for items to which previous ratings are not available. This paper utilizes
click data, which can be collected in abundance, to address the cold-start
problem. We propose a probabilistic item embedding model that learns item
representations from click data, and a model named EMB-MF, that connects it
with a probabilistic matrix factorization for rating prediction. The
experiments on three real-world datasets demonstrate that the proposed model is
not only effective in recommending items with no previous ratings, but also
outperforms competing methods, especially when the data is very sparse.Comment: ICONIP 201
Neural Attentive Session-based Recommendation
Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short
sessions. Previous work only considers the user's sequential behavior in the
current session, whereas the user's main purpose in the current session is not
emphasized. In this paper, we propose a novel neural networks framework, i.e.,
Neural Attentive Recommendation Machine (NARM), to tackle this problem.
Specifically, we explore a hybrid encoder with an attention mechanism to model
the user's sequential behavior and capture the user's main purpose in the
current session, which are combined as a unified session representation later.
We then compute the recommendation scores for each candidate item with a
bi-linear matching scheme based on this unified session representation. We
train NARM by jointly learning the item and session representations as well as
their matchings. We carried out extensive experiments on two benchmark
datasets. Our experimental results show that NARM outperforms state-of-the-art
baselines on both datasets. Furthermore, we also find that NARM achieves a
significant improvement on long sessions, which demonstrates its advantages in
modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and
Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939,
arXiv:1606.08117 by other author
A Line in the Sand: Recommendation or Ad-hoc Retrieval?
The popular approaches to recommendation and ad-hoc retrieval tasks are
largely distinct in the literature. In this work, we argue that many
recommendation problems can also be cast as ad-hoc retrieval tasks. To
demonstrate this, we build a solution for the RecSys 2018 Spotify challenge by
combining standard ad-hoc retrieval models and using popular retrieval tools
sets. We draw a parallel between the playlist continuation task and the task of
finding good expansion terms for queries in ad-hoc retrieval, and show that
standard pseudo-relevance feedback can be effective as a collaborative
filtering approach. We also use ad-hoc retrieval for content-based
recommendation by treating the input playlist title as a query and associating
all candidate tracks with meta-descriptions extracted from the background data.
The recommendations from these two approaches are further supplemented by a
nearest neighbor search based on track embeddings learned by a popular neural
model. Our final ranked list of recommendations is produced by a learning to
rank model. Our proposed solution using ad-hoc retrieval models achieved a
competitive performance on the music recommendation task at RecSys 2018
challenge---finishing at rank 7 out of 112 participating teams and at rank 5
out of 31 teams for the main and the creative tracks, respectively
Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling
Many social network applications depend on robust representations of
spatio-temporal data. In this work, we present an embedding model based on
feed-forward neural networks which transforms social media check-ins into dense
feature vectors encoding geographic, temporal, and functional aspects for
modelling places, neighborhoods, and users. We employ the embedding model in a
variety of applications including location recommendation, urban functional
zone study, and crime prediction. For location recommendation, we propose a
Spatio-Temporal Embedding Similarity algorithm (STES) based on the embedding
model.
In a range of experiments on real life data collected from Foursquare, we
demonstrate our model's effectiveness at characterizing places and people and
its applicability in aforementioned problem domains. Finally, we select eight
major cities around the globe and verify the robustness and generality of our
model by porting pre-trained models from one city to another, thereby
alleviating the need for costly local training
Try This Instead: Personalized and Interpretable Substitute Recommendation
As a fundamental yet significant process in personalized recommendation,
candidate generation and suggestion effectively help users spot the most
suitable items for them. Consequently, identifying substitutable items that are
interchangeable opens up new opportunities to refine the quality of generated
candidates. When a user is browsing a specific type of product (e.g., a laptop)
to buy, the accurate recommendation of substitutes (e.g., better equipped
laptops) can offer the user more suitable options to choose from, thus
substantially increasing the chance of a successful purchase. However, existing
methods merely treat this problem as mining pairwise item relationships without
the consideration of users' personal preferences. Moreover, the substitutable
relationships are implicitly identified through the learned latent
representations of items, leading to uninterpretable recommendation results. In
this paper, we propose attribute-aware collaborative filtering (A2CF) to
perform substitute recommendation by addressing issues from both
personalization and interpretability perspectives. Instead of directly
modelling user-item interactions, we extract explicit and polarized item
attributes from user reviews with sentiment analysis, whereafter the
representations of attributes, users, and items are simultaneously learned.
Then, by treating attributes as the bridge between users and items, we can
thoroughly model the user-item preferences (i.e., personalization) and
item-item relationships (i.e., substitution) for recommendation. In addition,
A2CF is capable of generating intuitive interpretations by analyzing which
attributes a user currently cares the most and comparing the recommended
substitutes with her/his currently browsed items at an attribute level. The
recommendation effectiveness and interpretation quality of A2CF are
demonstrated via extensive experiments on three real datasets.Comment: To appear in SIGIR'2
A Survey on Session-based Recommender Systems
Recommender systems (RSs) have been playing an increasingly important role
for informed consumption, services, and decision-making in the overloaded
information era and digitized economy. In recent years, session-based
recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different
from other RSs such as content-based RSs and collaborative filtering-based RSs
which usually model long-term yet static user preferences, SBRSs aim to capture
short-term but dynamic user preferences to provide more timely and accurate
recommendations sensitive to the evolution of their session contexts. Although
SBRSs have been intensively studied, neither unified problem statements for
SBRSs nor in-depth characterization of SBRS characteristics and challenges are
available. It is also unclear to what extent SBRS challenges have been
addressed and what the overall research landscape of SBRSs is. This
comprehensive review of SBRSs addresses the above aspects by exploring in depth
the SBRS entities (e.g., sessions), behaviors (e.g., users' clicks on items)
and their properties (e.g., session length). We propose a general problem
statement of SBRSs, summarize the diversified data characteristics and
challenges of SBRSs, and define a taxonomy to categorize the representative
SBRS research. Finally, we discuss new research opportunities in this exciting
and vibrant area.Comment: V2, a totally new versio
Temporal Learning and Sequence Modeling for a Job Recommender System
We present our solution to the job recommendation task for RecSys Challenge
2016. The main contribution of our work is to combine temporal learning with
sequence modeling to capture complex user-item activity patterns to improve job
recommendations. First, we propose a time-based ranking model applied to
historical observations and a hybrid matrix factorization over time re-weighted
interactions. Second, we exploit sequence properties in user-items activities
and develop a RNN-based recommendation model. Our solution achieved 5
place in the challenge among more than 100 participants. Notably, the strong
performance of our RNN approach shows a promising new direction in employing
sequence modeling for recommendation systems.Comment: a shorter version in proceedings of RecSys Challenge 201
Deep Interest Network for Click-Through Rate Prediction
Click-through rate prediction is an essential task in industrial
applications, such as online advertising. Recently deep learning based models
have been proposed, which follow a similar Embedding\&MLP paradigm. In these
methods large scale sparse input features are first mapped into low dimensional
embedding vectors, and then transformed into fixed-length vectors in a
group-wise manner, finally concatenated together to fed into a multilayer
perceptron (MLP) to learn the nonlinear relations among features. In this way,
user features are compressed into a fixed-length representation vector, in
regardless of what candidate ads are. The use of fixed-length vector will be a
bottleneck, which brings difficulty for Embedding\&MLP methods to capture
user's diverse interests effectively from rich historical behaviors. In this
paper, we propose a novel model: Deep Interest Network (DIN) which tackles this
challenge by designing a local activation unit to adaptively learn the
representation of user interests from historical behaviors with respect to a
certain ad. This representation vector varies over different ads, improving the
expressive ability of model greatly. Besides, we develop two techniques:
mini-batch aware regularization and data adaptive activation function which can
help training industrial deep networks with hundreds of millions of parameters.
Experiments on two public datasets as well as an Alibaba real production
dataset with over 2 billion samples demonstrate the effectiveness of proposed
approaches, which achieve superior performance compared with state-of-the-art
methods. DIN now has been successfully deployed in the online display
advertising system in Alibaba, serving the main traffic.Comment: Accepted by KDD 201
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