130 research outputs found
From Competition to Complementarity: Comparative Influence Diffusion and Maximization
Influence maximization is a well-studied problem that asks for a small set of
influential users from a social network, such that by targeting them as early
adopters, the expected total adoption through influence cascades over the
network is maximized. However, almost all prior work focuses on cascades of a
single propagating entity or purely-competitive entities. In this work, we
propose the Comparative Independent Cascade (Com-IC) model that covers the full
spectrum of entity interactions from competition to complementarity. In Com-IC,
users' adoption decisions depend not only on edge-level information
propagation, but also on a node-level automaton whose behavior is governed by a
set of model parameters, enabling our model to capture not only competition,
but also complementarity, to any possible degree. We study two natural
optimization problems, Self Influence Maximization and Complementary Influence
Maximization, in a novel setting with complementary entities. Both problems are
NP-hard, and we devise efficient and effective approximation algorithms via
non-trivial techniques based on reverse-reachable sets and a novel "sandwich
approximation". The applicability of both techniques extends beyond our model
and problems. Our experiments show that the proposed algorithms consistently
outperform intuitive baselines in four real-world social networks, often by a
significant margin. In addition, we learn model parameters from real user
action logs.Comment: An abridged of this work is to appear in the Proceedings of VLDB
Endowment (PVDLB), Vol 9, No 2. Also, the paper will be presented in the VLDB
2016 conference in New Delhi, India. This update contains new theoretical and
experimental results, and the paper is now in single-column format (44 pages
Influence Maximization with Bandits
We consider the problem of \emph{influence maximization}, the problem of
maximizing the number of people that become aware of a product by finding the
`best' set of `seed' users to expose the product to. Most prior work on this
topic assumes that we know the probability of each user influencing each other
user, or we have data that lets us estimate these influences. However, this
information is typically not initially available or is difficult to obtain. To
avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm
that estimates the influence probabilities as we sequentially try different
seed sets. We establish bounds on the performance of this procedure under the
existing edge-level feedback as well as a novel and more realistic node-level
feedback. Beyond our theoretical results, we describe a practical
implementation and experimentally demonstrate its efficiency and effectiveness
on four real datasets.Comment: 12 page
Validating Network Value of Influencers by means of Explanations
Recently, there has been significant interest in social influence analysis.
One of the central problems in this area is the problem of identifying
influencers, such that by convincing these users to perform a certain action
(like buying a new product), a large number of other users get influenced to
follow the action. The client of such an application is a marketer who would
target these influencers for marketing a given new product, say by providing
free samples or discounts. It is natural that before committing resources for
targeting an influencer the marketer would be interested in validating the
influence (or network value) of influencers returned. This requires digging
deeper into such analytical questions as: who are their followers, on what
actions (or products) they are influential, etc. However, the current
approaches to identifying influencers largely work as a black box in this
respect. The goal of this paper is to open up the black box, address these
questions and provide informative and crisp explanations for validating the
network value of influencers.
We formulate the problem of providing explanations (called PROXI) as a
discrete optimization problem of feature selection. We show that PROXI is not
only NP-hard to solve exactly, it is NP-hard to approximate within any
reasonable factor. Nevertheless, we show interesting properties of the
objective function and develop an intuitive greedy heuristic. We perform
detailed experimental analysis on two real world datasets - Twitter and
Flixster, and show that our approach is useful in generating concise and
insightful explanations of the influence distribution of users and that our
greedy algorithm is effective and efficient with respect to several baselines
Show Me the Money: Dynamic Recommendations for Revenue Maximization
Recommender Systems (RS) play a vital role in applications such as e-commerce
and on-demand content streaming. Research on RS has mainly focused on the
customer perspective, i.e., accurate prediction of user preferences and
maximization of user utilities. As a result, most existing techniques are not
explicitly built for revenue maximization, the primary business goal of
enterprises. In this work, we explore and exploit a novel connection between RS
and the profitability of a business. As recommendations can be seen as an
information channel between a business and its customers, it is interesting and
important to investigate how to make strategic dynamic recommendations leading
to maximum possible revenue. To this end, we propose a novel \model that takes
into account a variety of factors including prices, valuations, saturation
effects, and competition amongst products. Under this model, we study the
problem of finding revenue-maximizing recommendation strategies over a finite
time horizon. We show that this problem is NP-hard, but approximation
guarantees can be obtained for a slightly relaxed version, by establishing an
elegant connection to matroid theory. Given the prohibitively high complexity
of the approximation algorithm, we also design intelligent heuristics for the
original problem. Finally, we conduct extensive experiments on two real and
synthetic datasets and demonstrate the efficiency, scalability, and
effectiveness our algorithms, and that they significantly outperform several
intuitive baselines.Comment: Conference version published in PVLDB 7(14). To be presented in the
VLDB Conference 2015, in Hawaii. This version gives a detailed submodularity
proo
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