31,861 research outputs found
Fairness-Aware Recommendation of Information Curators
This paper highlights our ongoing efforts to create effective information
curator recommendation models that can be personalized for individual users,
while maintaining important fairness properties. Concretely, we introduce the
problem of information curator recommendation, provide a high-level overview of
a fairness-aware recommender, and introduce some preliminary experimental
evidence over a real-world Twitter dataset. We conclude with some thoughts on
future directions.Comment: 5 pages, 3 figures, The 2nd FATREC Workshop on Responsible
Recommendation at RecSys, 201
Personalized Thread Recommendation for MOOC Discussion Forums
Social learning, i.e., students learning from each other through social
interactions, has the potential to significantly scale up instruction in online
education. In many cases, such as in massive open online courses (MOOCs),
social learning is facilitated through discussion forums hosted by course
providers. In this paper, we propose a probabilistic model for the process of
learners posting on such forums, using point processes. Different from existing
works, our method integrates topic modeling of the post text, timescale
modeling of the decay in post activity over time, and learner topic interest
modeling into a single model, and infers this information from user data. Our
method also varies the excitation levels induced by posts according to the
thread structure, to reflect typical notification settings in discussion
forums. We experimentally validate the proposed model on three real-world MOOC
datasets, with the largest one containing up to 6,000 learners making 40,000
posts in 5,000 threads. Results show that our model excels at thread
recommendation, achieving significant improvement over a number of baselines,
thus showing promise of being able to direct learners to threads that they are
interested in more efficiently. Moreover, we demonstrate analytics that our
model parameters can provide, such as the timescales of different topic
categories in a course.Comment: To appear at ECML-PKDD 201
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
In many recommender systems, users and items are associated with attributes,
and users show preferences to items. The attribute information describes
users'(items') characteristics and has a wide range of applications, such as
user profiling, item annotation, and feature-enhanced recommendation. As
annotating user (item) attributes is a labor intensive task, the attribute
values are often incomplete with many missing attribute values. Therefore, item
recommendation and attribute inference have become two main tasks in these
platforms. Researchers have long converged that user (item) attributes and the
preference behavior are highly correlated. Some researchers proposed to
leverage one kind of data for the remaining task, and showed to improve
performance. Nevertheless, these models either neglected the incompleteness of
user (item) attributes or regarded the correlation of the two tasks with simple
models, leading to suboptimal performance of these two tasks. To this end, in
this paper, we define these two tasks in an attributed user-item bipartite
graph, and propose an Adaptive Graph Convolutional Network (AGCN) approach for
joint item recommendation and attribute inference. The key idea of AGCN is to
iteratively perform two parts: 1) Learning graph embedding parameters with
previously learned approximated attribute values to facilitate two tasks; 2)
Sending the approximated updated attribute values back to the attributed graph
for better graph embedding learning. Therefore, AGCN could adaptively adjust
the graph embedding learning parameters by incorporating both the given
attributes and the estimated attribute values, in order to provide weakly
supervised information to refine the two tasks. Extensive experimental results
on three real-world datasets clearly show the effectiveness of the proposed
model.Comment: Accepted by SIGIR202
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
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
Recommender Systems with Characterized Social Regularization
Social recommendation, which utilizes social relations to enhance recommender
systems, has been gaining increasing attention recently with the rapid
development of online social network. Existing social recommendation methods
are based on the fact that users preference or decision is influenced by their
social friends' behaviors. However, they assume that the influences of social
relation are always the same, which violates the fact that users are likely to
share preference on diverse products with different friends. In this paper, we
present a novel CSR (short for Characterized Social Regularization) model by
designing a universal regularization term for modeling variable social
influence. Our proposed model can be applied to both explicit and implicit
iteration. Extensive experiments on a real-world dataset demonstrate that CSR
significantly outperforms state-of-the-art social recommendation methods.Comment: to appear in CIKM 201
D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling
Exponential growth of social media consumption demands effective user
interests-social contents modeling for more personalized recommendation and
social media summarization. However, due to the heterogeneous nature of social
contents, traditional approaches lack the ability of capturing the hidden
semantic correlations across these multi-modal data, which leads to semantic
gaps between social content understanding and user interests. To effectively
bridge the semantic gaps, we propose a novel deep learning framework for user
interests-social contents modeling. We first mine and parse data, i.e. textual
content, visual content, social context and social relation, from heterogeneous
social media feeds. Then, we design a two-branch network to map the social
contents and users into a same latent space. Particularly, the network is
trained by a large margin objective that combines a cross-instance distance
constraint with a within-instance semantic-preserving constraint in an end-to-
end manner. At last, a Deep Semantic-Preserving Embedding (D-Sempre) is
learned, and the ranking results can be given by calculating distances between
social contents and users. To demonstrate the effectiveness of D-Sempre in user
interests-social contents modeling, we construct a Twitter dataset and conduct
extensive experiments on it. As a result, D-Sempre effectively integrates the
multi-modal data from heterogeneous social media feeds and captures the hidden
semantic correlations between users' interests and social contents.Comment: ACM Multimedi
Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation
Recently, there is a surge of social recommendation, which leverages social
relations among users to improve recommendation performance. However, in many
applications, social relations are absent or very sparse. Meanwhile, the
attribute information of users or items may be rich. It is a big challenge to
exploit these attribute information for the improvement of recommendation
performance. In this paper, we organize objects and relations in recommendation
system as a heterogeneous information network, and introduce meta path based
similarity measure to evaluate the similarity of users or items. Furthermore, a
matrix factorization based dual regularization framework SimMF is proposed to
flexibly integrate different types of information through adopting the
similarity of users and items as regularization on latent factors of users and
items. Extensive experiments not only validate the effectiveness of SimMF but
also reveal some interesting findings. We find that attribute information of
users and items can significantly improve recommendation accuracy, and their
contribution seems more important than that of social relations. The
experiments also reveal that different regularization models have obviously
different impact on users and items.Comment: 12 pages, 5 figure
A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews
Recommender systems (RSs) provide an effective way of alleviating the
information overload problem by selecting personalized choices. Online social
networks and user-generated content provide diverse sources for recommendation
beyond ratings, which present opportunities as well as challenges for
traditional RSs. Although social matrix factorization (Social MF) can integrate
ratings with social relations and topic matrix factorization can integrate
ratings with item reviews, both of them ignore some useful information. In this
paper, we investigate the effective data fusion by combining the two
approaches, in two steps. First, we extend Social MF to exploit the graph
structure of neighbors. Second, we propose a novel framework MR3 to jointly
model these three types of information effectively for rating prediction by
aligning latent factors and hidden topics. We achieve more accurate rating
prediction on two real-life datasets. Furthermore, we measure the contribution
of each data source to the proposed framework.Comment: 7 pages, 8 figure
From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews
Recommending products to consumers means not only understanding their tastes,
but also understanding their level of experience. For example, it would be a
mistake to recommend the iconic film Seven Samurai simply because a user enjoys
other action movies; rather, we might conclude that they will eventually enjoy
it -- once they are ready. The same is true for beers, wines, gourmet foods --
or any products where users have acquired tastes: the `best' products may not
be the most `accessible'. Thus our goal in this paper is to recommend products
that a user will enjoy now, while acknowledging that their tastes may have
changed over time, and may change again in the future. We model how tastes
change due to the very act of consuming more products -- in other words, as
users become more experienced. We develop a latent factor recommendation system
that explicitly accounts for each user's level of experience. We find that such
a model not only leads to better recommendations, but also allows us to study
the role of user experience and expertise on a novel dataset of fifteen million
beer, wine, food, and movie reviews.Comment: 11 pages, 7 figure
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