12,574 research outputs found
Meta-evaluation of online and offline web search evaluation metrics
As in most information retrieval (IR) studies, evaluation plays an essential part in Web search research. Both offline and online evaluation metrics are adopted in measuring the performance of search engines. Offline metrics are usually based on relevance judgments of query-document pairs from assessors while online metrics exploit the user behavior data, such as clicks, collected from search engines to compare search algorithms. Although both types of IR evaluation metrics have achieved success, to what extent can they predict user satisfaction still remains under-investigated. To shed light on this research question, we meta-evaluate a series of existing online and offline metrics to study how well they infer actual search user satisfaction in different search scenarios. We find that both types of evaluation metrics significantly correlate with user satisfaction while they reflect satisfaction from different perspectives for different search tasks. Offline metrics better align with user satisfaction in homogeneous search (i.e. ten blue links) whereas online metrics outperform when vertical results are federated. Finally, we also propose to incorporate mouse hover information into existing online evaluation metrics, and empirically show that they better align with search user satisfaction than click-based online metrics
Counterfactual Estimation and Optimization of Click Metrics for Search Engines
Optimizing an interactive system against a predefined online metric is
particularly challenging, when the metric is computed from user feedback such
as clicks and payments. The key challenge is the counterfactual nature: in the
case of Web search, any change to a component of the search engine may result
in a different search result page for the same query, but we normally cannot
infer reliably from search log how users would react to the new result page.
Consequently, it appears impossible to accurately estimate online metrics that
depend on user feedback, unless the new engine is run to serve users and
compared with a baseline in an A/B test. This approach, while valid and
successful, is unfortunately expensive and time-consuming. In this paper, we
propose to address this problem using causal inference techniques, under the
contextual-bandit framework. This approach effectively allows one to run
(potentially infinitely) many A/B tests offline from search log, making it
possible to estimate and optimize online metrics quickly and inexpensively.
Focusing on an important component in a commercial search engine, we show how
these ideas can be instantiated and applied, and obtain very promising results
that suggest the wide applicability of these techniques
Online Model Evaluation in a Large-Scale Computational Advertising Platform
Online media provides opportunities for marketers through which they can
deliver effective brand messages to a wide range of audiences. Advertising
technology platforms enable advertisers to reach their target audience by
delivering ad impressions to online users in real time. In order to identify
the best marketing message for a user and to purchase impressions at the right
price, we rely heavily on bid prediction and optimization models. Even though
the bid prediction models are well studied in the literature, the equally
important subject of model evaluation is usually overlooked. Effective and
reliable evaluation of an online bidding model is crucial for making faster
model improvements as well as for utilizing the marketing budgets more
efficiently. In this paper, we present an experimentation framework for bid
prediction models where our focus is on the practical aspects of model
evaluation. Specifically, we outline the unique challenges we encounter in our
platform due to a variety of factors such as heterogeneous goal definitions,
varying budget requirements across different campaigns, high seasonality and
the auction-based environment for inventory purchasing. Then, we introduce
return on investment (ROI) as a unified model performance (i.e., success)
metric and explain its merits over more traditional metrics such as
click-through rate (CTR) or conversion rate (CVR). Most importantly, we discuss
commonly used evaluation and metric summarization approaches in detail and
propose a more accurate method for online evaluation of new experimental models
against the baseline. Our meta-analysis-based approach addresses various
shortcomings of other methods and yields statistically robust conclusions that
allow us to conclude experiments more quickly in a reliable manner. We
demonstrate the effectiveness of our evaluation strategy on real campaign data
through some experiments.Comment: Accepted to ICDM201
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
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