20,293 research outputs found
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
Augmenting Recurrent Neural Networks with High-Order User-Contextual Preference for Session-Based Recommendation
The recent adoption of recurrent neural networks (RNNs) for session modeling
has yielded substantial performance gains compared to previous approaches. In
terms of context-aware session modeling, however, the existing RNN-based models
are limited in that they are not designed to explicitly model rich static
user-side contexts (e.g., age, gender, location). Therefore, in this paper, we
explore the utility of explicit user-side context modeling for RNN session
models. Specifically, we propose an augmented RNN (ARNN) model that extracts
high-order user-contextual preference using the product-based neural network
(PNN) in order to augment any existing RNN session model. Evaluation results
show that our proposed model outperforms the baseline RNN session model by a
large margin when rich user-side contexts are available
Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm
Route choice in multimodal networks shows a considerable variation between
different individuals as well as the current situational context.
Personalization of recommendation algorithms are already common in many areas,
e.g., online retail. However, most online routing applications still provide
shortest distance or shortest travel-time routes only, neglecting individual
preferences as well as the current situation. Both aspects are of particular
importance in a multimodal setting as attractivity of some transportation modes
such as biking crucially depends on personal characteristics and exogenous
factors like the weather. This paper introduces the FAVourite rOUte
Recommendation (FAVOUR) approach to provide personalized, situation-aware route
proposals based on three steps: first, at the initialization stage, the user
provides limited information (home location, work place, mobility options,
sociodemographics) used to select one out of a small number of initial
profiles. Second, based on this information, a stated preference survey is
designed in order to sharpen the profile. In this step a mass preference prior
is used to encode the prior knowledge on preferences from the class identified
in step one. And third, subsequently the profile is continuously updated during
usage of the routing services. The last two steps use Bayesian learning
techniques in order to incorporate information from all contributing
individuals. The FAVOUR approach is presented in detail and tested on a small
number of survey participants. The experimental results on this real-world
dataset show that FAVOUR generates better-quality recommendations w.r.t.
alternative learning algorithms from the literature. In particular the
definition of the mass preference prior for initialization of step two is shown
to provide better predictions than a number of alternatives from the
literature.Comment: 12 pages, 6 figures, 1 table. Submitted to IEEE Transactions on
Intelligent Transportation Systems journal for publicatio
Fairness-Aware Recommendation of Information Curators
This paper highlights our ongoing efforts to create effective information
curator recommendation models that can be personalized for individual users,
while maintaining important fairness properties. Concretely, we introduce the
problem of information curator recommendation, provide a high-level overview of
a fairness-aware recommender, and introduce some preliminary experimental
evidence over a real-world Twitter dataset. We conclude with some thoughts on
future directions.Comment: 5 pages, 3 figures, The 2nd FATREC Workshop on Responsible
Recommendation at RecSys, 201
Visually-Aware Fashion Recommendation and Design with Generative Image Models
Building effective recommender systems for domains like fashion is
challenging due to the high level of subjectivity and the semantic complexity
of the features involved (i.e., fashion styles). Recent work has shown that
approaches to `visual' recommendation (e.g.~clothing, art, etc.) can be made
more accurate by incorporating visual signals directly into the recommendation
objective, using `off-the-shelf' feature representations derived from deep
networks. Here, we seek to extend this contribution by showing that
recommendation performance can be significantly improved by learning `fashion
aware' image representations directly, i.e., by training the image
representation (from the pixel level) and the recommender system jointly; this
contribution is related to recent work using Siamese CNNs, though we are able
to show improvements over state-of-the-art recommendation techniques such as
BPR and variants that make use of pre-trained visual features. Furthermore, we
show that our model can be used \emph{generatively}, i.e., given a user and a
product category, we can generate new images (i.e., clothing items) that are
most consistent with their personal taste. This represents a first step towards
building systems that go beyond recommending existing items from a product
corpus, but which can be used to suggest styles and aid the design of new
products.Comment: 10 pages, 6 figures. Accepted by ICDM'17 as a long pape
MMALFM: Explainable Recommendation by Leveraging Reviews and Images
Although the latent factor model achieves good accuracy in rating prediction,
it suffers from many problems including cold-start, non-transparency, and
suboptimal results for individual user-item pairs. In this paper, we exploit
textual reviews and item images together with ratings to tackle these
limitations. Specifically, we first apply a proposed multi-modal aspect-aware
topic model (MATM) on text reviews and item images to model users' preferences
and items' features from different aspects, and also estimate the aspect
importance of a user towards an item. Then the aspect importance is integrated
into a novel aspect-aware latent factor model (ALFM), which learns user's and
item's latent factors based on ratings. In particular, ALFM introduces a weight
matrix to associate those latent factors with the same set of aspects in MATM,
such that the latent factors could be used to estimate aspect ratings. Finally,
the overall rating is computed via a linear combination of the aspect ratings,
which are weighted by the corresponding aspect importance. To this end, our
model could alleviate the data sparsity problem and gain good interpretability
for recommendation. Besides, every aspect rating is weighted by its aspect
importance, which is dependent on the targeted user's preferences and the
targeted item's features. Therefore, it is expected that the proposed method
can model a user's preferences on an item more accurately for each user-item
pair. Comprehensive experimental studies have been conducted on the Yelp 2017
Challenge dataset and Amazon product datasets to demonstrate the effectiveness
of our method.Comment: This paper has been accepted by Transactions on Information Systems.
arXiv admin note: substantial text overlap with arXiv:1802.0793
Attribute-aware Collaborative Filtering: Survey and Classification
Attribute-aware CF models aims at rating prediction given not only the
historical rating from users to items, but also the information associated with
users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This
paper surveys works in the past decade developing attribute-aware CF systems,
and discovered that mathematically they can be classified into four different
categories. We provide the readers not only the high level mathematical
interpretation of the existing works in this area but also the mathematical
insight for each category of models. Finally we provide in-depth experiment
results comparing the effectiveness of the major works in each category
From Word Embeddings to Item Recommendation
Social network platforms can use the data produced by their users to serve
them better. One of the services these platforms provide is recommendation
service. Recommendation systems can predict the future preferences of users
using their past preferences. In the recommendation systems literature there
are various techniques, such as neighborhood based methods, machine-learning
based methods and matrix-factorization based methods. In this work, a set of
well known methods from natural language processing domain, namely Word2Vec, is
applied to recommendation systems domain. Unlike previous works that use
Word2Vec for recommendation, this work uses non-textual features, the
check-ins, and it recommends venues to visit/check-in to the target users. For
the experiments, a Foursquare check-in dataset is used. The results show that
use of continuous vector space representations of items modeled by techniques
of Word2Vec is promising for making recommendations
A Jointly Learned Context-Aware Place of Interest Embedding for Trip Recommendations
Trip recommendation is an important location-based service that helps relieve
users from the time and efforts for trip planning. It aims to recommend a
sequence of places of interest (POIs) for a user to visit that maximizes the
user's satisfaction. When adding a POI to a recommended trip, it is essential
to understand the context of the recommendation, including the POI popularity,
other POIs co-occurring in the trip, and the preferences of the user. These
contextual factors are learned separately in existing studies, while in
reality, they impact jointly on a user's choice of a POI to visit. In this
study, we propose a POI embedding model to jointly learn the impact of these
contextual factors. We call the learned POI embedding a context-aware POI
embedding. To showcase the effectiveness of this embedding, we apply it to
generate trip recommendations given a user and a time budget. We propose two
trip recommendation algorithms based on our context-aware POI embedding. The
first algorithm finds the exact optimal trip by transforming and solving the
trip recommendation problem as an integer linear programming problem. To
achieve a high computation efficiency, the second algorithm finds a
heuristically optimal trip based on adaptive large neighborhood search. We
perform extensive experiments on real datasets. The results show that our
proposed algorithms consistently outperform state-of-the-art algorithms in trip
recommendation quality, with an advantage of up to 43% in F1-score
A Survey of Point-of-interest Recommendation in Location-based Social Networks
Point-of-interest (POI) recommendation that suggests new places for users to
visit arises with the popularity of location-based social networks (LBSNs). Due
to the importance of POI recommendation in LBSNs, it has attracted much
academic and industrial interest. In this paper, we offer a systematic review
of this field, summarizing the contributions of individual efforts and
exploring their relations. We discuss the new properties and challenges in POI
recommendation, compared with traditional recommendation problems, e.g., movie
recommendation. Then, we present a comprehensive review in three aspects:
influential factors for POI recommendation, methodologies employed for POI
recommendation, and different tasks in POI recommendation. Specifically, we
propose three taxonomies to classify POI recommendation systems. First, we
categorize the systems by the influential factors check-in characteristics,
including the geographical information, social relationship, temporal
influence, and content indications. Second, we categorize the systems by the
methodology, including systems modeled by fused methods and joint methods.
Third, we categorize the systems as general POI recommendation and successive
POI recommendation by subtle differences in the recommendation task whether to
be bias to the recent check-in. For each category, we summarize the
contributions and system features, and highlight the representative work.
Moreover, we discuss the available data sets and the popular metrics. Finally,
we point out the possible future directions in this area and conclude this
survey
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