92 research outputs found
Mapping Wiki User Contribution Types to Motivations for Participation: A Case Study
Different classifications of Wiki editor have been proposed. However, so far there
has been no mapping between user classes based on their contributions and their motivations,
which can be useful to design persuasive functions in wiki systems to increase participation.
In this study, we attempt to bridge this gap by developing a customized MediaWiki system,
used by 10 senior undergraduate students for their coursework. The participants were
classified into three editors' classes and mapped to their motivation factors, using the system
data and the results from the exit questionnaire
Improved Bounds for Sampling Solutions of Random CNF Formulas
Let be a random -CNF formula on variables and clauses,
where each clause is a disjunction of literals chosen independently and
uniformly. Our goal is to sample an approximately uniform solution of
(or equivalently, approximate the partition function of ).
Let be the density. The previous best algorithm runs in time
for any [Galanis,
Goldberg, Guo, and Yang, SIAM J. Comput.'21]. Our result significantly improves
both bounds by providing an almost-linear time sampler for any
.
The density captures the \emph{average degree} in the random
formula. In the worst-case model with bounded \emph{maximum degree}, current
best efficient sampler works up to degree bound [He, Wang, and Yin,
FOCS'22 and SODA'23], which is, for the first time, superseded by its
average-case counterpart due to our bound. Our result is the first
progress towards establishing the intuition that the solvability of the
average-case model (random -CNF formula with bounded average degree) is
better than the worst-case model (standard -CNF formula with bounded maximal
degree) in terms of sampling solutions.Comment: 51 pages, all proofs added, and bounds slightly improve
Fourier Growth of Parity Decision Trees
We prove that for every parity decision tree of depth d on n variables, the sum of absolute values of Fourier coefficients at level ? is at most d^{?/2} ? O(? ? log(n))^?. Our result is nearly tight for small values of ? and extends a previous Fourier bound for standard decision trees by Sherstov, Storozhenko, and Wu (STOC, 2021).
As an application of our Fourier bounds, using the results of Bansal and Sinha (STOC, 2021), we show that the k-fold Forrelation problem has (randomized) parity decision tree complexity ??(n^{1-1/k}), while having quantum query complexity ? k/2?.
Our proof follows a random-walk approach, analyzing the contribution of a random path in the decision tree to the level-? Fourier expression. To carry the argument, we apply a careful cleanup procedure to the parity decision tree, ensuring that the value of the random walk is bounded with high probability. We observe that step sizes for the level-? walks can be computed by the intermediate values of level ? ?-1 walks, which calls for an inductive argument. Our approach differs from previous proofs of Tal (FOCS, 2020) and Sherstov, Storozhenko, and Wu (STOC, 2021) that relied on decompositions of the tree. In particular, for the special case of standard decision trees we view our proof as slightly simpler and more intuitive.
In addition, we prove a similar bound for noisy decision trees of cost at most d - a model that was recently introduced by Ben-David and Blais (FOCS, 2020)
How buyers perceive the credibility of advisors in online marketplace: review balance, review amount and misattribution
In an online marketplace, buyers rely heavily on reviews posted by previous buyers (referred to as advisors). The advisor’s credibility determines the persuasiveness of reviews. Much work has addressed the evaluation of advisors’ credibility based on their static profile information, but little attention has been paid to the effect of the information about the history of advisors’ reviews. We conducted three sub-studies to evaluate how the advisors’ review balance (proportion of positive reviews) affects the buyer’s judgement of advisor’s credibility (e.g., trustworthiness, expertise). The result of study 1 shows that advisors with mixed positive and negative reviews are perceived to be more trustworthy, and those with extremely positive or negative review balance are perceived to be less trustworthy. Moreover, the perceived expertise of the advisor increases as the review balance turns from positive to negative; yet buyers perceive advisors with extremely negative review balance as low in expertise. Study 2 finds that buyers might be more inclined to misattribute low trustworthiness to low expertise when they are processing high number of reviews. Finally, study 3 explains the misattribution phenomenon and suggests that perceived expertise has close relationship with affective trust. Both theoretical and practical implications are discussed
Improved bounds for the sunflower lemma
A sunflower with petals is a collection of sets so that the
intersection of each pair is equal to the intersection of all. Erd\H{o}s and
Rado proved the sunflower lemma: for any fixed , any family of sets of size
, with at least about sets, must contain a sunflower. The famous
sunflower conjecture is that the bound on the number of sets can be improved to
for some constant . In this paper, we improve the bound to about
. In fact, we prove the result for a robust notion of sunflowers,
for which the bound we obtain is tight up to lower order terms.Comment: Revised preprint, added sections on applications and rainbow
sunflower
How do you feel when you see a list of prices? the interplay among price dispersion, perceived risk and initial trust in Chinese C2C market
The issues of trust fraud, product genuineness and price dispersion jointly make Chinese C2C
buyers difficult to identify trustworthy sellers with a low price. Little is known about the
generation of initial trust when buyers search products and receive lists of widely ranged prices.
This study proposes a theoretical model to explain how price dispersion interacts with other
factors in C2C purchase, such as initial trust, perceived risk, perceived value and purchase
intention. Product type is considered as a moderator. 261 students were invited in a survey-based
experiment. The results from PLS analysis show that price dispersion negatively affects perceived
value, whilst, positively affects perceived risk, which further influences perceived value negatively.
Price dispersion also negatively influences initial trust through perceived risk. Moreover, the
negative effects of price dispersion are stronger when buyers purchase high-touch products
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