7,792 research outputs found
PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset
Recommendation systems create personalized list of items that might interest the user
by analyzing the user’s history of past purchases and/or consumption. Generally only
a small subset of the items are assessed by each user, and from the large subset of
unseen items, the systems need to produce an accurate list of recommendations.
For rating based systems, most of the traditional methods for recommendation
focus on the absolute ratings provided by the users to the items. In this work,
we extend the traditional Matrix Factorization approach for recommendation and
propose pairwise relation based factor modeling. We propose the method based on
the pairwise preferences between the items to capture the relative tendency of user
selecting one item over the other.
While modeling the items in the system, the use of pairwise preferences allow
information flow between the items through the preference relations as an additional
information. Item feedbacks are available in the form of reviews apart from the
rating information. The reviews have textual information that can be really helpful
to represent the item’s latent feature vector appropriately. We perform topic modeling
of the item reviews and use the topic vectors to guide the joint factor modeling of the
users and items and learn their final representations. The proposed methods shows
promising results in comparison to the state-of-the-art methods in our experiments.
v
Clustering and Inference From Pairwise Comparisons
Given a set of pairwise comparisons, the classical ranking problem computes a
single ranking that best represents the preferences of all users. In this
paper, we study the problem of inferring individual preferences, arising in the
context of making personalized recommendations. In particular, we assume that
there are users of types; users of the same type provide similar
pairwise comparisons for items according to the Bradley-Terry model. We
propose an efficient algorithm that accurately estimates the individual
preferences for almost all users, if there are
pairwise comparisons per type, which is near optimal in sample complexity when
only grows logarithmically with or . Our algorithm has three steps:
first, for each user, compute the \emph{net-win} vector which is a projection
of its -dimensional vector of pairwise comparisons onto an
-dimensional linear subspace; second, cluster the users based on the net-win
vectors; third, estimate a single preference for each cluster separately. The
net-win vectors are much less noisy than the high dimensional vectors of
pairwise comparisons and clustering is more accurate after the projection as
confirmed by numerical experiments. Moreover, we show that, when a cluster is
only approximately correct, the maximum likelihood estimation for the
Bradley-Terry model is still close to the true preference.Comment: Corrected typos in the abstrac
PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset
Recommendation systems create personalized list of items that
might interest the user by analyzing the user’s history of past purchases
and/or consumption. For rating based systems, most of the
traditional methods for recommendation focus on the absolute ratings
provided by the users to the items. In this paper, we extend the
traditional Matrix Factorization approach for recommendation and
propose pairwise relation based factor modeling. While modeling
the items in the system, the use of pairwise preferences allow information
flow between the items through the preference relations
as an additional information. Item feedbacks are available in the
form of reviews apart from the rating information. The reviews
have textual information that can be really helpful to represent
the item’s latent feature vector appropriately. We perform topic
modeling of the item reviews and use the topic vectors to guide the
joint factor modeling of the users and items and learn their final
representations. The proposed method shows promising results in
comparison to the state-of-the-art methods in our experiments
{ELIXIR}: {L}earning from User Feedback on Explanations to Improve Recommender Models
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback
Neural Collaborative Ranking
Recommender systems are aimed at generating a personalized ranked list of
items that an end user might be interested in. With the unprecedented success
of deep learning in computer vision and speech recognition, recently it has
been a hot topic to bridge the gap between recommender systems and deep neural
network. And deep learning methods have been shown to achieve state-of-the-art
on many recommendation tasks. For example, a recent model, NeuMF, first
projects users and items into some shared low-dimensional latent feature space,
and then employs neural nets to model the interaction between the user and item
latent features to obtain state-of-the-art performance on the recommendation
tasks. NeuMF assumes that the non-interacted items are inherent negative and
uses negative sampling to relax this assumption. In this paper, we examine an
alternative approach which does not assume that the non-interacted items are
necessarily negative, just that they are less preferred than interacted items.
Specifically, we develop a new classification strategy based on the widely used
pairwise ranking assumption. We combine our classification strategy with the
recently proposed neural collaborative filtering framework, and propose a
general collaborative ranking framework called Neural Network based
Collaborative Ranking (NCR). We resort to a neural network architecture to
model a user's pairwise preference between items, with the belief that neural
network will effectively capture the latent structure of latent factors. The
experimental results on two real-world datasets show the superior performance
of our models in comparison with several state-of-the-art approaches.Comment: Proceedings of the 2018 ACM on Conference on Information and
Knowledge Managemen
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