227,206 research outputs found
Uncertainty and technical efficiency in Finnish agriculture: a state-contingent approach
In this article, we present one of the first real-world empirical applications of state-contingent production theory. Our state-contingent behavioral model allows us to analyze production under both inefficiency and uncertainty without regard to the nature of producer risk preferences. Using farm data for Finland, we estimate a flexible production model that permits substitutability between state-contingent outputs. We test empirically, and reject, an assumption that has been implicit in almost all efficiency studies conducted in the last three decades, namely that the production technology is output-cubical, i.e., that outputs are not substitutable between states of nature.state-contingent; production; uncertainty
Recurrent Poisson Factorization for Temporal Recommendation
Poisson factorization is a probabilistic model of users and items for
recommendation systems, where the so-called implicit consumer data is modeled
by a factorized Poisson distribution. There are many variants of Poisson
factorization methods who show state-of-the-art performance on real-world
recommendation tasks. However, most of them do not explicitly take into account
the temporal behavior and the recurrent activities of users which is essential
to recommend the right item to the right user at the right time. In this paper,
we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
the classical PF methods by utilizing a Poisson process for modeling the
implicit feedback. RPF treats time as a natural constituent of the model and
brings to the table a rich family of time-sensitive factorization models. To
elaborate, we instantiate several variants of RPF who are capable of handling
dynamic user preferences and item specification (DRPF), modeling the
social-aspect of product adoption (SRPF), and capturing the consumption
heterogeneity among users and items (HRPF). We also develop a variational
algorithm for approximate posterior inference that scales up to massive data
sets. Furthermore, we demonstrate RPF's superior performance over many
state-of-the-art methods on synthetic dataset, and large scale real-world
datasets on music streaming logs, and user-item interactions in M-Commerce
platforms.Comment: Submitted to KDD 2017 | Halifax, Nova Scotia - Canada - sigkdd, Codes
are available at https://github.com/AHosseini/RP
Click-aware purchase prediction with push at the top
Eliciting user preferences from purchase records for performing purchase
prediction is challenging because negative feedback is not explicitly observed,
and because treating all non-purchased items equally as negative feedback is
unrealistic. Therefore, in this study, we present a framework that leverages
the past click records of users to compensate for the missing user-item
interactions of purchase records, i.e., non-purchased items. We begin by
formulating various model assumptions, each one assuming a different order of
user preferences among purchased, clicked-but-not-purchased, and non-clicked
items, to study the usefulness of leveraging click records. We implement the
model assumptions using the Bayesian personalized ranking model, which
maximizes the area under the curve for bipartite ranking. However, we argue
that using click records for bipartite ranking needs a meticulously designed
model because of the relative unreliableness of click records compared with
that of purchase records. Therefore, we ultimately propose a novel
learning-to-rank method, called P3Stop, for performing purchase prediction. The
proposed model is customized to be robust to relatively unreliable click
records by particularly focusing on the accuracy of top-ranked items.
Experimental results on two real-world e-commerce datasets demonstrate that
P3STop considerably outperforms the state-of-the-art implicit-feedback-based
recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see
https://doi.org/10.1016/j.ins.2020.02.06
PLM-IPE: A Pixel-Landmark Mutual Enhanced Framework for Implicit Preference Estimation
In this paper, we are interested in understanding how customers perceive fashion recommendations, in particular when observing a proposed combination of garments to compose an outfit. Automatically understanding how a suggested item is perceived, without any kind of active engagement, is in fact an essential block to achieve interactive applications. We propose a pixel-landmark mutual enhanced framework for implicit preference estimation, named PLM-IPE, which is capable of inferring the user's implicit preferences exploiting visual cues, without any active or conscious engagement. PLM-IPE consists of three key modules: pixel-based estimator, landmark-based estimator and mutual learning based optimization. The former two modules work on capturing the implicit reaction of the user from the pixel level and landmark level, respectively. The last module serves to transfer knowledge between the two parallel estimators. Towards evaluation, we collected a real-world dataset, named SentiGarment, which contains 3,345 facial reaction videos paired with suggested outfits and human labeled reaction scores. Extensive experiments show the superiority of our model over state-of-the-art approaches
Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes
Music preferences are strongly shaped by the cultural and socio-economic
background of the listener, which is reflected, to a considerable extent, in
country-specific music listening profiles. Previous work has already identified
several country-specific differences in the popularity distribution of music
artists listened to. In particular, what constitutes the "music mainstream"
strongly varies between countries. To complement and extend these results, the
article at hand delivers the following major contributions: First, using
state-of-the-art unsupervised learning techniques, we identify and thoroughly
investigate (1) country profiles of music preferences on the fine-grained level
of music tracks (in contrast to earlier work that relied on music preferences
on the artist level) and (2) country archetypes that subsume countries sharing
similar patterns of listening preferences. Second, we formulate four user
models that leverage the user's country information on music preferences. Among
others, we propose a user modeling approach to describe a music listener as a
vector of similarities over the identified country clusters or archetypes.
Third, we propose a context-aware music recommendation system that leverages
implicit user feedback, where context is defined via the four user models. More
precisely, it is a multi-layer generative model based on a variational
autoencoder, in which contextual features can influence recommendations through
a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation
system and user models on a real-world corpus of more than one billion
listening records of users around the world (out of which we use 369 million in
our experiments) and show its merits vis-a-vis state-of-the-art algorithms that
do not exploit this type of context information.Comment: 30 pages, 3 tables, 12 figure
Inferring latent network from cascade data for dynamic social recommendation
© 2016 IEEE. Social recommendation explores social information to improve the quality of a recommender system. It can be further divided into explicit and implicit social network recommendation. The former assumes the existence of explicit social connections between users in addition to the rating data. The latter one assumes the availability of only the ratings but not the social connections between users since the explicit social information data may not necessarily be available and usually are binary decision values (e.g., whether two people are friends), while the strength of their relationships is missing. Most of the works in this field use only rating data to infer the latent social networks. They ignore the dynamic nature of users that the preferences of users drift over time distinctly. To this end, we propose a new Implicit Dynamic Social Recommendation (IDSR) model, which infers latent social network from cascade data. It can sufficiently mine the information contained in time by mining the cascade data and identify the dynamic changes in the users in time by using the latest updated social network to make recommendations. Experiments and comparisons on three real-world datasets show that the proposed model outperforms the state-of-The-Art solutions in both explicit and implicit scenarios
SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
The recent development of online recommender systems has a focus on
collaborative ranking from implicit feedback, such as user clicks and
purchases. Different from explicit ratings, which reflect graded user
preferences, the implicit feedback only generates positive and unobserved
labels. While considerable efforts have been made in this direction, the
well-known pairwise and listwise approaches have still been limited by various
challenges. Specifically, for the pairwise approaches, the assumption of
independent pairwise preference is not always held in practice. Also, the
listwise approaches cannot efficiently accommodate "ties" due to the
precondition of the entire list permutation. To this end, in this paper, we
propose a novel setwise Bayesian approach for collaborative ranking, namely
SetRank, to inherently accommodate the characteristics of implicit feedback in
recommender system. Specifically, SetRank aims at maximizing the posterior
probability of novel setwise preference comparisons and can be implemented with
matrix factorization and neural networks. Meanwhile, we also present the
theoretical analysis of SetRank to show that the bound of excess risk can be
proportional to , where and are the numbers of items and
users, respectively. Finally, extensive experiments on four real-world datasets
clearly validate the superiority of SetRank compared with various
state-of-the-art baselines.Comment: This paper has been accepted in AAAI'2
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