3,572 research outputs found
Visually-aware Recommendation with Aesthetic Features
Visual information plays a critical role in human decision-making process.
While recent developments on visually-aware recommender systems have taken the
product image into account, none of them has considered the aesthetic aspect.
We argue that the aesthetic factor is very important in modeling and predicting
users' preferences, especially for some fashion-related domains like clothing
and jewelry. This work addresses the need of modeling aesthetic information in
visually-aware recommender systems. Technically speaking, we make three key
contributions in leveraging deep aesthetic features: (1) To describe the
aesthetics of products, we introduce the aesthetic features extracted from
product images by a deep aesthetic network. We incorporate these features into
recommender system to model users' preferences in the aesthetic aspect. (2)
Since in clothing recommendation, time is very important for users to make
decision, we design a new tensor decomposition model for implicit feedback
data. The aesthetic features are then injected to the basic tensor model to
capture the temporal dynamics of aesthetic preferences (e.g., seasonal
patterns). (3) We also use the aesthetic features to optimize the learning
strategy on implicit feedback data. We enrich the pairwise training samples by
considering the similarity among items in the visual space and graph space; the
key idea is that a user may likely have similar perception on similar items. We
perform extensive experiments on several real-world datasets and demonstrate
the usefulness of aesthetic features and the effectiveness of our proposed
methods.Comment: Accepted by VLDBJ. arXiv admin note: substantial text overlap with
arXiv:1809.0582
Hybrid Recommender Systems: A Systematic Literature Review
Recommender systems are software tools used to generate and provide
suggestions for items and other entities to the users by exploiting various
strategies. Hybrid recommender systems combine two or more recommendation
strategies in different ways to benefit from their complementary advantages.
This systematic literature review presents the state of the art in hybrid
recommender systems of the last decade. It is the first quantitative review
work completely focused in hybrid recommenders. We address the most relevant
problems considered and present the associated data mining and recommendation
techniques used to overcome them. We also explore the hybridization classes
each hybrid recommender belongs to, the application domains, the evaluation
process and proposed future research directions. Based on our findings, most of
the studies combine collaborative filtering with another technique often in a
weighted way. Also cold-start and data sparsity are the two traditional and top
problems being addressed in 23 and 22 studies each, while movies and movie
datasets are still widely used by most of the authors. As most of the studies
are evaluated by comparisons with similar methods using accuracy metrics,
providing more credible and user oriented evaluations remains a typical
challenge. Besides this, newer challenges were also identified such as
responding to the variation of user context, evolving user tastes or providing
cross-domain recommendations. Being a hot topic, hybrid recommenders represent
a good basis with which to respond accordingly by exploring newer opportunities
such as contextualizing recommendations, involving parallel hybrid algorithms,
processing larger datasets, etc.Comment: 38 pages, 9 figures, 14 tables. The final authenticated version is
available online at
https://content.iospress.com/articles/intelligent-data-analysis/ida16320
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
Proceedings of the 17th Dutch-Belgian Information Retrieval Workshop
This volume contains the papers presented at DIR 2018: 17th Dutch-Belgian
Information Retrieval Workshop (DIR) held on November 23, 2018 in Leiden. DIR
aims to serve as an international platform (with a special focus on the
Netherlands and Belgium) for exchange and discussions on research &
applications in the field of information retrieval and related fields.
The committee accepted 4 short papers presenting novel work, 3 demo
proposals, and 8 compressed contributions (summaries of papers recently
published in international journals and conferences). Each submission was
reviewed by at least 3 programme committee members
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
Combining Aspects of Genetic Algorithms with Weighted Recommender Hybridization
Recommender systems are established means to inspire users to watch
interesting movies, discover baby names, or read books. The recommendation
quality further improves by combining the results of multiple recommendation
algorithms using hybridization methods. In this paper, we focus on the task of
combining unscored recommendations into a single ensemble. Our proposed method
is inspired by genetic algorithms. It repeatedly selects items from the
recommendations to create a population of items that will be used for the final
ensemble. We compare our method with a weighted voting method and test the
performance of both in a movie- and name-recommendation scenario. We were able
to outperform the weighted method on both datasets by 20.3 % and 31.1 % and
decreased the overall execution time by up to 19.9 %. Our results do not only
propose a new kind of hybridization method, but introduce the field of
recommender hybridization to further work with genetic algorithms.Comment: 10 pages, 6 figures, 2 tables, iiWAS '17, December 4-6, 2017,
Salzburg, Austri
Parallel and Distributed Collaborative Filtering: A Survey
Collaborative filtering is amongst the most preferred techniques when
implementing recommender systems. Recently, great interest has turned towards
parallel and distributed implementations of collaborative filtering algorithms.
This work is a survey of the parallel and distributed collaborative filtering
implementations, aiming not only to provide a comprehensive presentation of the
field's development, but also to offer future research orientation by
highlighting the issues that need to be further developed.Comment: 46 page
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset
Today, recommender systems are an inevitable part of everyone's daily digital
routine and are present on most internet platforms. State-of-the-art deep
learning-based models require a large number of data to achieve their best
performance. Many datasets fulfilling this criterion have been proposed for
multiple domains, such as Amazon products, restaurants, or beers. However,
works and datasets in the hotel domain are limited: the largest hotel review
dataset is below the million samples. Additionally, the hotel domain suffers
from a higher data sparsity than traditional recommendation datasets and
therefore, traditional collaborative-filtering approaches cannot be applied to
such data. In this paper, we propose HotelRec, a very large-scale hotel
recommendation dataset, based on TripAdvisor, containing 50 million reviews. To
the best of our knowledge, HotelRec is the largest publicly available dataset
in the hotel domain (50M versus 0.9M) and additionally, the largest
recommendation dataset in a single domain and with textual reviews (50M versus
22M). We release HotelRec for further research:
https://github.com/Diego999/HotelRec.Comment: 7 pages, 3 figure, 5 tables. Accepted at LREC 202
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