17,057 research outputs found
Anticipating Information Needs Based on Check-in Activity
In this work we address the development of a smart personal assistant that is
capable of anticipating a user's information needs based on a novel type of
context: the person's activity inferred from her check-in records on a
location-based social network. Our main contribution is a method that
translates a check-in activity into an information need, which is in turn
addressed with an appropriate information card. This task is challenging
because of the large number of possible activities and related information
needs, which need to be addressed in a mobile dashboard that is limited in
size. Our approach considers each possible activity that might follow after the
last (and already finished) activity, and selects the top information cards
such that they maximize the likelihood of satisfying the user's information
needs for all possible future scenarios. The proposed models also incorporate
knowledge about the temporal dynamics of information needs. Using a combination
of historical check-in data and manual assessments collected via crowdsourcing,
we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM '17), 201
Bias characterization, assessment, and mitigation in location-based recommender systems
Location-Based Social Networks stimulated the rise of services such as Location-based Recommender Systems. These systems suggest to users points of interest (or venues) to visit when they arrive in a specific city or region. These recommendations impact various stakeholders in society, like the users who receive the recommendations and venue owners. Hence, if a recommender generates biased or polarized results, this affects in tangible ways both the experience of the users and the providers’ activities. In this paper, we focus on four forms of polarization, namely venue popularity, category popularity, venue exposure, and geographical distance. We characterize them on different families of recommendation algorithms when using a realistic (temporal-aware) offline evaluation methodology while assessing their existence. Besides, we propose two automatic approaches to mitigate those biases. Experimental results on real-world data show that these approaches are able to jointly improve the recommendation effectiveness, while alleviating these multiple polarizationsOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This
work has been funded by the Ministerio de Ciencia e Innovación (reference PID2019-108965GB-I00) and
by the European Social Fund (ESF), within the 2017 call for predoctoral contract
Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms
The performance of recommendation algorithms is closely tied to key
characteristics of the data sets they use, such as sparsity, popularity bias,
and preference distributions. In this paper, we conduct a comprehensive
explanatory analysis to shed light on the impact of a broad range of data
characteristics within the point-of-interest (POI) recommendation domain. To
accomplish this, we extend prior methodologies used to characterize traditional
recommendation problems by introducing new explanatory variables specifically
relevant to POI recommendation. We subdivide a POI recommendation data set on
New York City into domain-driven subsamples to measure the effect of varying
these characteristics on different state-of-the-art POI recommendation
algorithms in terms of accuracy, novelty, and item exposure. Our findings,
obtained through the application of an explanatory framework employing
multiple-regression models, reveal that the relevant independent variables
encompass all categories of data characteristics and account for as much as
85-90\% of the accuracy and item exposure achieved by the algorithms.
Our study reaffirms the pivotal role of prominent data characteristics, such as
density, popularity bias, and the distribution of check-ins in POI
recommendation. Additionally, we unveil novel factors, such as the proximity of
user activity to the city center and the duration of user activity. In summary,
our work reveals why certain POI recommendation algorithms excel in specific
recommendation problems and, conversely, offers practical insights into which
data characteristics should be modified (or explicitly recognized) to achieve
better performance
Mitigating Epidemics through Mobile Micro-measures
Epidemics of infectious diseases are among the largest threats to the quality
of life and the economic and social well-being of developing countries. The
arsenal of measures against such epidemics is well-established, but costly and
insufficient to mitigate their impact. In this paper, we argue that mobile
technology adds a powerful weapon to this arsenal, because (a) mobile devices
endow us with the unprecedented ability to measure and model the detailed
behavioral patterns of the affected population, and (b) they enable the
delivery of personalized behavioral recommendations to individuals in real
time. We combine these two ideas and propose several strategies to generate
such recommendations from mobility patterns. The goal of each strategy is a
large reduction in infections, with a small impact on the normal course of
daily life. We evaluate these strategies over the Orange D4D dataset and show
the benefit of mobile micro-measures, even if only a fraction of the population
participates. These preliminary results demonstrate the potential of mobile
technology to complement other measures like vaccination and quarantines
against disease epidemics.Comment: Presented at NetMob 2013, Bosto
LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
State-of-the-art item recommendation algorithms, which apply
Factorization Machines (FM) as a scoring function and
pairwise ranking loss as a trainer (PRFM for short), have
been recently investigated for the implicit feedback based
context-aware recommendation problem (IFCAR). However,
good recommenders particularly emphasize on the accuracy
near the top of the ranked list, and typical pairwise loss functions
might not match well with such a requirement. In this
paper, we demonstrate, both theoretically and empirically,
PRFM models usually lead to non-optimal item recommendation
results due to such a mismatch. Inspired by the success
of LambdaRank, we introduce Lambda Factorization
Machines (LambdaFM), which is particularly intended for
optimizing ranking performance for IFCAR. We also point
out that the original lambda function suffers from the issue
of expensive computational complexity in such settings due
to a large amount of unobserved feedback. Hence, instead
of directly adopting the original lambda strategy, we create
three effective lambda surrogates by conducting a theoretical
analysis for lambda from the top-N optimization perspective.
Further, we prove that the proposed lambda surrogates
are generic and applicable to a large set of pairwise
ranking loss functions. Experimental results demonstrate
LambdaFM significantly outperforms state-of-the-art algorithms
on three real-world datasets in terms of four standard
ranking measures
BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation
Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation
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