376 research outputs found
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
A Meta-Evaluation of C/W/L/A Metrics: System Ranking Similarity, System Ranking Consistency and Discriminative Power
Recently, Moffat et al. proposed an analytic framework, namely C/W/L/A, for
offline evaluation metrics. This framework allows information retrieval (IR)
researchers to design evaluation metrics through the flexible combination of
user browsing models and user gain aggregations. However, the statistical
stability of C/W/L/A metrics with different aggregations is not yet
investigated. In this study, we investigate the statistical stability of
C/W/L/A metrics from the perspective of: (1) the system ranking similarity
among aggregations, (2) the system ranking consistency of aggregations and (3)
the discriminative power of aggregations. More specifically, we combined
various aggregation functions with the browsing model of Precision, Discounted
Cumulative Gain (DCG), Rank-Biased Precision (RBP), INST, Average Precision
(AP) and Expected Reciprocal Rank (ERR), examing their performances in terms of
system ranking similarity, system ranking consistency and discriminative power
on two offline test collections. Our experimental result suggests that, in
terms of system ranking consistency and discriminative power, the aggregation
function of expected rate of gain (ERG) has an outstanding performance while
the aggregation function of maximum relevance usually has an insufficient
performance. The result also suggests that Precision, DCG, RBP, INST and AP
with their canonical aggregation all have favourable performances in system
ranking consistency and discriminative power; but for ERR, replacing its
canonical aggregation with ERG can further strengthen the discriminative power
while obtaining a system ranking list similar to the canonical version at the
same time
Applying the KISS principle for the CLEF-IP 2010 prior art candidate patent search task
We present our experiments and results for the DCU CNGL
participation in the CLEF-IP 2010 Candidate Patent Search Task. Our work applied standard information retrieval (IR) techniques to patent search. In addition, a very simple citation extraction method was applied to improve the
results. This was our second consecutive participation in the CLEF-IP tasks. Our experiments in 2009 showed that many sophisticated approach to IR do not improve the retrieval effectiveness for this task. For this reason of we decided
to apply only simple methods in 2010. These were demonstrated to be highly competitive with other participants. DCU submitted three runs for the Prior Art
Candidate Search Task, two of these runs achieved the second and third ranks among the 25 runs submitted by nine different participants. Our best run achieved MAP of 0.203, recall of 0.618, and PRES of 0.523
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
WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking
We present WISER, a new semantic search engine for expert finding in
academia. Our system is unsupervised and it jointly combines classical language
modeling techniques, based on text evidences, with the Wikipedia Knowledge
Graph, via entity linking.
WISER indexes each academic author through a novel profiling technique which
models her expertise with a small, labeled and weighted graph drawn from
Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the
author's publications, whereas the weighted edges express the semantic
relatedness among these entities computed via textual and graph-based
relatedness functions. Every node is also labeled with a relevance score which
models the pertinence of the corresponding entity to author's expertise, and is
computed by means of a proper random-walk calculation over that graph; and with
a latent vector representation which is learned via entity and other kinds of
structural embeddings derived from Wikipedia.
At query time, experts are retrieved by combining classic document-centric
approaches, which exploit the occurrences of query terms in the author's
documents, with a novel set of profile-centric scoring strategies, which
compute the semantic relatedness between the author's expertise and the query
topic via the above graph-based profiles.
The effectiveness of our system is established over a large-scale
experimental test on a standard dataset for this task. We show that WISER
achieves better performance than all the other competitors, thus proving the
effectiveness of modelling author's profile via our "semantic" graph of
entities. Finally, we comment on the use of WISER for indexing and profiling
the whole research community within the University of Pisa, and its application
to technology transfer in our University
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