241 research outputs found
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
Personalized Expert Recommendation: Models and Algorithms
Many large-scale information sharing systems including social media systems, questionanswering
sites and rating and reviewing applications have been growing rapidly, allowing
millions of human participants to generate and consume information on an unprecedented
scale. To manage the sheer growth of information generation, there comes the need to enable
personalization of information resources for users — to surface high-quality content
and feeds, to provide personally relevant suggestions, and so on. A fundamental task in
creating and supporting user-centered personalization systems is to build rich user profile
to aid recommendation for better user experience.
Therefore, in this dissertation research, we propose models and algorithms to facilitate
the creation of new crowd-powered personalized information sharing systems. Specifically,
we first give a principled framework to enable personalization of resources so that
information seekers can be matched with customized knowledgeable users based on their
previous historical actions and contextual information; We then focus on creating rich
user models that allows accurate and comprehensive modeling of user profiles for long
tail users, including discovering user’s known-for profile, user’s opinion bias and user’s
geo-topic profile. In particular, this dissertation research makes two unique contributions:
First, we introduce the problem of personalized expert recommendation and propose
the first principled framework for addressing this problem. To overcome the sparsity issue,
we investigate the use of user’s contextual information that can be exploited to build robust
models of personal expertise, study how spatial preference for personally-valuable expertise
varies across regions, across topics and based on different underlying social communities,
and integrate these different forms of preferences into a matrix factorization-based
personalized expert recommender.
Second, to support the personalized recommendation on experts, we focus on modeling
and inferring user profiles in online information sharing systems. In order to tap
the knowledge of most majority of users, we provide frameworks and algorithms to accurately
and comprehensively create user models by discovering user’s known-for profile,
user’s opinion bias and user’s geo-topic profile, with each described shortly as follows:
—We develop a probabilistic model called Bayesian Contextual Poisson Factorization
to discover what users are known for by others. Our model considers as input a small fraction
of users whose known-for profiles are already known and the vast majority of users for
whom we have little (or no) information, learns the implicit relationships between user?s
known-for profiles and their contextual signals, and finally predict known-for profiles for
those majority of users.
—We explore user’s topic-sensitive opinion bias, propose a lightweight semi-supervised
system called “BiasWatch” to semi-automatically infer the opinion bias of long-tail users,
and demonstrate how user’s opinion bias can be exploited to recommend other users with
similar opinion in social networks.
— We study how a user’s topical profile varies geo-spatially and how we can model
a user’s geo-spatial known-for profile as the last step in our dissertation for creation of
rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to
overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating
user contexts into the two-layered hierarchical user model for better representation
of user’s geo-topic preference by others
Hybrid Recommender for Online Petitions with Social Network and Psycholinguistic Features
The online petition has become one of the most important channels of civic participation. Most of the state-of-the-art online platforms, however, tend to use simple indicators (such as popularity) to rank petitions, hence creating a situation where the most popular petitions dominate the rank and attract most people’s attention. For the petitions which focus on specific issues, they are often in a disadvantageous position on the list. For example, a petition for local environment problem may not be seen by many people who are really concerned with it, simply because it takes multiple pages to reach it. Therefore, the simple ranking mechanism adopted by most of the online petition platforms cannot effectively link most petitions with those who are really concerned with them. According to previous studies online, petitions seriousness has been questioned due to the rare chance of succeeding. At most, less than 10% of online petitions get the chance to fulfill their causes.
To solve this problem, we present a design of a novel recommender system (PETREC). It leverages social interaction features, psycholinguistic features, and latent topic features to provide a personalized ranking to different users. Hence, it can give users better petition recommendations fitting their unique concerns. We evaluate PETREC against matrix factorization collaborative filtering and content-based filtering with the bag of words (Bow) features as two baseline recommenders for benchmarking. PETREC prediction performance outperformed Matrix factorization collaborative filtering, Bow petition-based content filtering, and Bow user-based content filtering with 4.2%, 1.7%, and 2.8% respectively as improvements in Root Mean Square Error (RMSE).
The recommendation system described in this paper has potential to improve the user experience of online petition platforms. Thus, it is possible that it could encourage more public participation. Eventually, it will help the citizens to make a real difference through actively participating in online petitions that are matching their personalized concerns
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Following recent successes in exploiting both latent factor and word
embedding models in recommendation, we propose a novel Regularized
Multi-Embedding (RME) based recommendation model that simultaneously
encapsulates the following ideas via decomposition: (1) which items a user
likes, (2) which two users co-like the same items, (3) which two items users
often co-liked, and (4) which two items users often co-disliked. In
experimental validation, the RME outperforms competing state-of-the-art models
in both explicit and implicit feedback datasets, significantly improving
Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition,
under the cold-start scenario for users with the lowest number of interactions,
against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in
MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source
code are available at: https://github.com/thanhdtran/RME.git.Comment: CIKM 201
Mitigating Impact of Fake News
Fake news in social media is a pressing issue. The goal of this project was to mitigate the impact of fake news by developing recommendation systems that can recommend fact-checking URLs to “guardians”. We created three recommendation systems, improved the GAU model, determined the best recommendation system, and developed a web application that facilitates use of the recommendation systems
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