18,695 research outputs found
A personalized and context-aware news offer for mobile devices
For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer
Salience and Market-aware Skill Extraction for Job Targeting
At LinkedIn, we want to create economic opportunity for everyone in the
global workforce. To make this happen, LinkedIn offers a reactive Job Search
system, and a proactive Jobs You May Be Interested In (JYMBII) system to match
the best candidates with their dream jobs. One of the most challenging tasks
for developing these systems is to properly extract important skill entities
from job postings and then target members with matched attributes. In this
work, we show that the commonly used text-based \emph{salience and
market-agnostic} skill extraction approach is sub-optimal because it only
considers skill mention and ignores the salient level of a skill and its market
dynamics, i.e., the market supply and demand influence on the importance of
skills. To address the above drawbacks, we present \model, our deployed
\emph{salience and market-aware} skill extraction system. The proposed \model
~shows promising results in improving the online performance of job
recommendation (JYMBII) ( job apply) and skill suggestions for job
posters ( suggestion rejection rate). Lastly, we present case studies to
show interesting insights that contrast traditional skill recognition method
and the proposed \model~from occupation, industry, country, and individual
skill levels. Based on the above promising results, we deployed the \model
~online to extract job targeting skills for all M job postings served at
LinkedIn.Comment: 9 pages, to appear in KDD202
Diverse Weighted Bipartite b-Matching
Bipartite matching, where agents on one side of a market are matched to
agents or items on the other, is a classical problem in computer science and
economics, with widespread application in healthcare, education, advertising,
and general resource allocation. A practitioner's goal is typically to maximize
a matching market's economic efficiency, possibly subject to some fairness
requirements that promote equal access to resources. A natural balancing act
exists between fairness and efficiency in matching markets, and has been the
subject of much research.
In this paper, we study a complementary goal---balancing diversity and
efficiency---in a generalization of bipartite matching where agents on one side
of the market can be matched to sets of agents on the other. Adapting a
classical definition of the diversity of a set, we propose a quadratic
programming-based approach to solving a supermodular minimization problem that
balances diversity and total weight of the solution. We also provide a scalable
greedy algorithm with theoretical performance bounds. We then define the price
of diversity, a measure of the efficiency loss due to enforcing diversity, and
give a worst-case theoretical bound. Finally, we demonstrate the efficacy of
our methods on three real-world datasets, and show that the price of diversity
is not bad in practice
Knowledge-aware Complementary Product Representation Learning
Learning product representations that reflect complementary relationship
plays a central role in e-commerce recommender system. In the absence of the
product relationships graph, which existing methods rely on, there is a need to
detect the complementary relationships directly from noisy and sparse customer
purchase activities. Furthermore, unlike simple relationships such as
similarity, complementariness is asymmetric and non-transitive. Standard usage
of representation learning emphasizes on only one set of embedding, which is
problematic for modelling such properties of complementariness. We propose
using knowledge-aware learning with dual product embedding to solve the above
challenges. We encode contextual knowledge into product representation by
multi-task learning, to alleviate the sparsity issue. By explicitly modelling
with user bias terms, we separate the noise of customer-specific preferences
from the complementariness. Furthermore, we adopt the dual embedding framework
to capture the intrinsic properties of complementariness and provide geometric
interpretation motivated by the classic separating hyperplane theory. Finally,
we propose a Bayesian network structure that unifies all the components, which
also concludes several popular models as special cases. The proposed method
compares favourably to state-of-art methods, in downstream classification and
recommendation tasks. We also develop an implementation that scales efficiently
to a dataset with millions of items and customers
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