29,652 research outputs found
Unbiased Learning to Rank with Unbiased Propensity Estimation
Learning to rank with biased click data is a well-known challenge. A variety
of methods has been explored to debias click data for learning to rank such as
click models, result interleaving and, more recently, the unbiased
learning-to-rank framework based on inverse propensity weighting. Despite their
differences, most existing studies separate the estimation of click bias
(namely the \textit{propensity model}) from the learning of ranking algorithms.
To estimate click propensities, they either conduct online result
randomization, which can negatively affect the user experience, or offline
parameter estimation, which has special requirements for click data and is
optimized for objectives (e.g. click likelihood) that are not directly related
to the ranking performance of the system. In this work, we address those
problems by unifying the learning of propensity models and ranking models. We
find that the problem of estimating a propensity model from click data is a
dual problem of unbiased learning to rank. Based on this observation, we
propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker
and an \textit{unbiased propensity model}. DLA is an automatic unbiased
learning-to-rank framework as it directly learns unbiased ranking models from
biased click data without any preprocessing. It can adapt to the change of bias
distributions and is applicable to online learning. Our empirical experiments
with synthetic and real-world data show that the models trained with DLA
significantly outperformed the unbiased learning-to-rank algorithms based on
result randomization and the models trained with relevance signals extracted by
click models
Why People Search for Images using Web Search Engines
What are the intents or goals behind human interactions with image search
engines? Knowing why people search for images is of major concern to Web image
search engines because user satisfaction may vary as intent varies. Previous
analyses of image search behavior have mostly been query-based, focusing on
what images people search for, rather than intent-based, that is, why people
search for images. To date, there is no thorough investigation of how different
image search intents affect users' search behavior.
In this paper, we address the following questions: (1)Why do people search
for images in text-based Web image search systems? (2)How does image search
behavior change with user intent? (3)Can we predict user intent effectively
from interactions during the early stages of a search session? To this end, we
conduct both a lab-based user study and a commercial search log analysis.
We show that user intents in image search can be grouped into three classes:
Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals
different user behavior patterns under these three intents, such as first click
time, query reformulation, dwell time and mouse movement on the result page.
Based on user interaction features during the early stages of an image search
session, that is, before mouse scroll, we develop an intent classifier that is
able to achieve promising results for classifying intents into our three intent
classes. Given that all features can be obtained online and unobtrusively, the
predicted intents can provide guidance for choosing ranking methods immediately
after scrolling
Whole-Chain Recommendations
With the recent prevalence of Reinforcement Learning (RL), there have been
tremendous interests in developing RL-based recommender systems. In practical
recommendation sessions, users will sequentially access multiple scenarios,
such as the entrance pages and the item detail pages, and each scenario has its
specific characteristics. However, the majority of existing RL-based
recommender systems focus on optimizing one strategy for all scenarios or
separately optimizing each strategy, which could lead to sub-optimal overall
performance. In this paper, we study the recommendation problem with multiple
(consecutive) scenarios, i.e., whole-chain recommendations. We propose a
multi-agent RL-based approach (DeepChain), which can capture the sequential
correlation among different scenarios and jointly optimize multiple
recommendation strategies. To be specific, all recommender agents (RAs) share
the same memory of users' historical behaviors, and they work collaboratively
to maximize the overall reward of a session. Note that optimizing multiple
recommendation strategies jointly faces two challenges in the existing
model-free RL model - (i) it requires huge amounts of user behavior data, and
(ii) the distribution of reward (users' feedback) are extremely unbalanced. In
this paper, we introduce model-based RL techniques to reduce the training data
requirement and execute more accurate strategy updates. The experimental
results based on a real e-commerce platform demonstrate the effectiveness of
the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge
Managemen
Users' reading habits in online news portals
The aim of this study is to survey reading habits of users of an online news portal. The assumption motivating this study is that insight into the reading habits of users can be helpful to design better news recommendation systems. We estimated the transition probabilities that users who read an article of one news category will move to read an article of another (not necessarily distinct) news category. For this, we analyzed the users' click behavior within plista data set. Key findings are the popularity of category local, loyalty of readers to the same category, observing similar results when addressing enforced click streams, and the case that click behavior is highly influenced by the news category
Predicting Session Length in Media Streaming
Session length is a very important aspect in determining a user's
satisfaction with a media streaming service. Being able to predict how long a
session will last can be of great use for various downstream tasks, such as
recommendations and ad scheduling. Most of the related literature on user
interaction duration has focused on dwell time for websites, usually in the
context of approximating post-click satisfaction either in search results, or
display ads. In this work we present the first analysis of session length in a
mobile-focused online service, using a real world data-set from a major music
streaming service. We use survival analysis techniques to show that the
characteristics of the length distributions can differ significantly between
users, and use gradient boosted trees with appropriate objectives to predict
the length of a session using only information available at its beginning. Our
evaluation on real world data illustrates that our proposed technique
outperforms the considered baseline.Comment: 4 pages, 3 figure
Dynamic modeling of web purchase behavior and e-mailing impact by Petri net
In this article, the authors introduce Petri nets to model the dynamics of web site visits and purchase behaviors in the case of wish list systems. They describe web site activities and their transition with probability distributions and model the sequential impact of influential factors through links that better explain web purchase behavior dynamics. The basic model, which analyzes site connections and purchases to explain visit and purchase behavior, performs better than a classical negative binomial regression model. To demonstrate its flexibility, the authors extend the wish list Petri net model to measure the impact of e-mailing intervals on visit frequency and purchase.internet; wish list; e-mail; Petri net; dynamic model
A Content Analysis of Youth Internet Safety Programs: Are Effective Prevention Strategies Being Used?
ABSTRACT: Almost half of youth in the U.S. report receiving internet safety education (ISE) in their schools. Unfortunately, we know little about what educational messages make a difference in problems such as cyberbullying, sexting, or online predators. To consider directions for improving effectiveness, a content analysis was conducted on materials from four ISE programs. Results indicate that ISE programs are mostly not incorporating proven educational strategies. Common ISE messages have proliferated without a clear rationale for why they would be effective. It is recommended that program developers and other stakeholders reconsider ISE messages, improve educational strategies, and participate in evaluation. The field must also consider whether ISE messages would be better delivered through broader youth safety prevention programs versus stand-alone lessons
Effects of Chlordiazepoxide on Predator Odor-Induced Reductions of Playfulness in Juvenile Rats
The extent to which a non-sedative dose of chlordiazepoxide (CDP) is able to modify the behavioral responses toward a predator odor was assessed in juvenile rats. Play behavior was suppressed and defensive behaviors were enhanced in the presence of a collar previously worn by a cat, when tested 24 hours later in the same context as that where the exposure occurred, and when tested in a context different than that in which the exposure occurred for up to 3 hours after exposure. CDP had no effect on the ability of cat odor to suppress play when rats were tested in the presence of the odor or when tested 24 hours later in the same context where that exposure occurred. When rats were exposed to a worn cat collar in their home cage and tested in a different context CDP attenuated the ability of cat odor to reduce one measure of play (nape contacts) but not another measure (pins). Rats had an opportunity to hide during testing and CDP either decreased hiding or increased risk assessment from within the hide box in all of the testing scenarios. These data suggest that CDP can alter the defensive strategy used by juvenile rats that are confronted with a predatory threat and can also lead to an earlier return to pre-threat levels of playfulness when that threat becomes less immediate
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