3,802 research outputs found
Temporal Proximity induces Attributes Similarity
Users consume their favorite content in temporal proximity of consumption
bundles according to their preferences and tastes. Thus, the underlying
attributes of items implicitly match user preferences, however, current
recommender systems largely ignore this fundamental driver in identifying
matching items. In this work, we introduce a novel temporal proximity filtering
method to enable items-matching. First, we demonstrate that proximity
preferences exist. Second, we present an induced similarity metric in temporal
proximity driven by user tastes and third, we show that this induced similarity
can be used to learn items pairwise similarity in attribute space. The proposed
model does not rely on any knowledge outside users' consumption bundles and
provide a novel way to devise user preferences and tastes driven novel items
recommender
Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machine
learning in industry, with many services using them to match users to products
or information. As such it is important to ask: what are the possible fairness
risks, how can we quantify them, and how should we address them? In this paper
we offer a set of novel metrics for evaluating algorithmic fairness concerns in
recommender systems. In particular we show how measuring fairness based on
pairwise comparisons from randomized experiments provides a tractable means to
reason about fairness in rankings from recommender systems. Building on this
metric, we offer a new regularizer to encourage improving this metric during
model training and thus improve fairness in the resulting rankings. We apply
this pairwise regularization to a large-scale, production recommender system
and show that we are able to significantly improve the system's pairwise
fairness
Graph-based Collaborative Ranking
Data sparsity, that is a common problem in neighbor-based collaborative
filtering domain, usually complicates the process of item recommendation. This
problem is more serious in collaborative ranking domain, in which calculating
the users similarities and recommending items are based on ranking data. Some
graph-based approaches have been proposed to address the data sparsity problem,
but they suffer from two flaws. First, they fail to correctly model the users
priorities, and second, they cannot be used when the only available data is a
set of ranking instead of rating values. In this paper, we propose a novel
graph-based approach, called GRank, that is designed for collaborative ranking
domain. GRank can correctly model users priorities in a new tripartite graph
structure, and analyze it to directly infer a recommendation list. The
experimental results show a significant improvement in recommendation quality
compared to the state of the art graph-based recommendation algorithms and
other collaborative ranking techniques
IteRank: An iterative network-oriented approach to neighbor-based collaborative ranking
Neighbor-based collaborative ranking (NCR) techniques follow three
consecutive steps to recommend items to each target user: first they calculate
the similarities among users, then they estimate concordance of pairwise
preferences to the target user based on the calculated similarities. Finally,
they use estimated pairwise preferences to infer the total ranking of items for
the target user. This general approach faces some problems as the rank data is
usually sparse as users usually have compared only a few pairs of items and
consequently, the similarities among users is calculated based on limited
information and is not accurate enough for inferring true values of preference
concordance and can lead to an invalid ranking of items. This article presents
a novel framework, called IteRank, that models the data as a bipartite network
containing users and pairwise preferences. It then simultaneously refines
users' similarities and preferences' concordances using a random walk method on
this graph structure. It uses the information in this first step in another
network structure for simultaneously adjusting the concordances of preferences
and rankings of items. Using this approach, IteRank can overcome some existing
problems caused by the sparsity of the data. Experimental results show that
IteRank improves the performance of recommendation compared to the state of the
art NCR techniques that use the traditional NCR framework for recommendation
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
Reliable graph-based collaborative ranking
GRank is a recent graph-based recommendation approach the uses a novel
heterogeneous information network to model users' priorities and analyze it to
directly infer a recommendation list. Unfortunately, GRank neglects the
semantics behind different types of paths in the network and during the
process, it may use unreliable paths that are inconsistent with the general
idea of similarity in neighborhood collaborative ranking. That negligence
undermines the reliability of the recommendation list generated by GRank. This
paper seeks to present a novel framework for reliable graph-based collaborative
ranking, called ReGRank, that ranks items based on reliable recommendation
paths that are in harmony with the semantics behind different approaches in
neighborhood collaborative ranking. To our knowledge, ReGRank is the first
unified framework for neighborhood collaborative ranking that in addition to
traditional user-based collaborative ranking, can also be adapted for
preference-based and representative-based collaborative ranking as well.
Experimental results show that ReGRank significantly improves the state-of-the
art neighborhood and graph-based collaborative ranking algorithms.Comment: 30 pages, 9 figures, 3 Table
Yum-me: A Personalized Nutrient-based Meal Recommender System
Nutrient-based meal recommendations have the potential to help individuals
prevent or manage conditions such as diabetes and obesity. However, learning
people's food preferences and making recommendations that simultaneously appeal
to their palate and satisfy nutritional expectations are challenging. Existing
approaches either only learn high-level preferences or require a prolonged
learning period. We propose Yum-me, a personalized nutrient-based meal
recommender system designed to meet individuals' nutritional expectations,
dietary restrictions, and fine-grained food preferences. Yum-me enables a
simple and accurate food preference profiling procedure via a visual quiz-based
user interface, and projects the learned profile into the domain of
nutritionally appropriate food options to find ones that will appeal to the
user. We present the design and implementation of Yum-me, and further describe
and evaluate two innovative contributions. The first contriution is an open
source state-of-the-art food image analysis model, named FoodDist. We
demonstrate FoodDist's superior performance through careful benchmarking and
discuss its applicability across a wide array of dietary applications. The
second contribution is a novel online learning framework that learns food
preference from item-wise and pairwise image comparisons. We evaluate the
framework in a field study of 227 anonymous users and demonstrate that it
outperforms other baselines by a significant margin. We further conducted an
end-to-end validation of the feasibility and effectiveness of Yum-me through a
60-person user study, in which Yum-me improves the recommendation acceptance
rate by 42.63%
SibRank: Signed Bipartite Network Analysis for Neighbor-based Collaborative Ranking
Collaborative ranking is an emerging field of recommender systems that
utilizes users' preference data rather than rating values. Unfortunately,
neighbor-based collaborative ranking has gained little attention despite its
more flexibility and justifiability. This paper proposes a novel framework,
called SibRank that seeks to improve the state of the art neighbor-based
collaborative ranking methods. SibRank represents users' preferences as a
signed bipartite network, and finds similar users, through a novel personalized
ranking algorithm in signed networks
General factorization framework for context-aware recommendations
Context-aware recommendation algorithms focus on refining recommendations by
considering additional information, available to the system. This topic has
gained a lot of attention recently. Among others, several factorization methods
were proposed to solve the problem, although most of them assume explicit
feedback which strongly limits their real-world applicability. While these
algorithms apply various loss functions and optimization strategies, the
preference modeling under context is less explored due to the lack of tools
allowing for easy experimentation with various models. As context dimensions
are introduced beyond users and items, the space of possible preference models
and the importance of proper modeling largely increases.
In this paper we propose a General Factorization Framework (GFF), a single
flexible algorithm that takes the preference model as an input and computes
latent feature matrices for the input dimensions. GFF allows us to easily
experiment with various linear models on any context-aware recommendation task,
be it explicit or implicit feedback based. The scaling properties makes it
usable under real life circumstances as well.
We demonstrate the framework's potential by exploring various preference
models on a 4-dimensional context-aware problem with contexts that are
available for almost any real life datasets. We show in our experiments --
performed on five real life, implicit feedback datasets -- that proper
preference modelling significantly increases recommendation accuracy, and
previously unused models outperform the traditional ones. Novel models in GFF
also outperform state-of-the-art factorization algorithms.
We also extend the method to be fully compliant to the Multidimensional
Dataspace Model, one of the most extensive data models of context-enriched
data. Extended GFF allows the seamless incorporation of information into the
fac[truncated]Comment: The final publication is available at Springer via
http://dx.doi.org/10.1007/s10618-015-0417-y. Data Mining and Knowledge
Discovery, 201
A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders
Multi-objective recommender systems address the difficult task of
recommending items that are relevant to multiple, possibly conflicting,
criteria. However these systems are most often designed to address the
objective of one single stakeholder, typically, in online commerce, the
consumers whose input and purchasing decisions ultimately determine the success
of the recommendation systems. In this work, we address the multi-objective,
multi-stakeholder, recommendation problem involving one or more objective(s)
per stakeholder. In addition to the consumer stakeholder, we also consider two
other stakeholders; the suppliers who provide the goods and services for sale
and the intermediary who is responsible for helping connect consumers to
suppliers via its recommendation algorithms. We analyze the multi-objective,
multi-stakeholder, problem from the point of view of the online marketplace
intermediary whose objective is to maximize its commission through its
recommender system. We define a multi-objective problem relating all our three
stakeholders which we solve with a novel learning-to-re-rank approach that
makes use of a novel regularization function based on the Kendall tau
correlation metric and its kernel version; given an initial ranking of item
recommendations built for the consumer, we aim to re-rank it such that the new
ranking is also optimized for the secondary objectives while staying close to
the initial ranking. We evaluate our approach on a real-world dataset of hotel
recommendations provided by Expedia where we show the effectiveness of our
approach against a business-rules oriented baseline model.Comment: Presented at the 2017 Workshop on Value-Aware and Multistakeholder
Recommendatio
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