7,023 research outputs found
Optimizing and Evaluating Stream-Based News Recommendation Algorithms
Abstract. Due to the overwhelming amount of items and information users need support in finding the information matching the individual preferences and ex-pectations. Real-time stream-based recommender systems get in the focus of research allowing the adaption of recommendations to the user’s context and the current set of relevant items. In this paper we focus on recommending news articles. In contrast to most traditional recommender systems, our system must handle several additional challenges: News articles have a short lifecycle forcing the recommender system to continuously adapt to the set of news articles. In addition, the recommender algorithms should work efficiently: On the one hand, news recommendations must be provided within milliseconds since the recom-mendations must be embedded in news article pages. On the other hand, the news algorithms must be able to handle a huge amount of recommendation request in order to process load peaks without violating the time constraints. We present algorithms optimized for providing real-time news recommendation given limited hardware resources. We present an offline evaluating framework allowing us the efficient optimizing of recommender algorithms taking into account the available hardware resources. The evaluation shows that our approach allows us to find optimal recommender algorithms for a given hardware setting.
CLEF 2017 NewsREEL Overview: Offline and Online Evaluation of Stream-based News Recommender Systems
The CLEF NewsREEL challenge allows researchers to evaluate news
recommendation algorithms both online (NewsREEL Live) and offline (News-
REEL Replay). Compared with the previous year NewsREEL challenged participants
with a higher volume of messages and new news portals. In the 2017
edition of the CLEF NewsREEL challenge a wide variety of new approaches have
been implemented ranging from the use of existing machine learning frameworks,
to ensemble methods to the use of deep neural networks. This paper gives an
overview over the implemented approaches and discusses the evaluation results.
In addition, the main results of Living Lab and the Replay task are explained
Overview of CLEF NEWSREEL 2014: News Recommendations Evaluation Labs
This paper summarises objectives, organisation, and results of the first
news recommendation evaluation lab (NEWSREEL 2014). NEWSREEL targeted
the evaluation of news recommendation algorithms in the form of a campaignstyle
evaluation lab. Participants had the chance to apply two types of evaluation
schemes. On the one hand, participants could apply their algorithms onto a data
set. We refer to this setting as off-line evaluation. On the other hand, participants
could deploy their algorithms on a server to interactively receive recommendation
requests. We refer to this setting as on-line evaluation. This setting ought to reveal
the actual performance of recommendation methods. The competition strived to
illustrate differences between evaluation with historical data and actual users. The
on-line evaluation does reflect all requirements which active recommender systems
face in practise. These requirements include real-time responses and large-scale
data volumes. We present the competition’s results and discuss commonalities
regarding participants’ approaches
A Contextual-Bandit Approach to Personalized News Article Recommendation
Personalized web services strive to adapt their services (advertisements,
news articles, etc) to individual users by making use of both content and user
information. Despite a few recent advances, this problem remains challenging
for at least two reasons. First, web service is featured with dynamically
changing pools of content, rendering traditional collaborative filtering
methods inapplicable. Second, the scale of most web services of practical
interest calls for solutions that are both fast in learning and computation.
In this work, we model personalized recommendation of news articles as a
contextual bandit problem, a principled approach in which a learning algorithm
sequentially selects articles to serve users based on contextual information
about the users and articles, while simultaneously adapting its
article-selection strategy based on user-click feedback to maximize total user
clicks.
The contributions of this work are three-fold. First, we propose a new,
general contextual bandit algorithm that is computationally efficient and well
motivated from learning theory. Second, we argue that any bandit algorithm can
be reliably evaluated offline using previously recorded random traffic.
Finally, using this offline evaluation method, we successfully applied our new
algorithm to a Yahoo! Front Page Today Module dataset containing over 33
million events. Results showed a 12.5% click lift compared to a standard
context-free bandit algorithm, and the advantage becomes even greater when data
gets more scarce.Comment: 10 pages, 5 figure
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Rankings are the primary interface through which many online platforms match
users to items (e.g. news, products, music, video). In these two-sided markets,
not only the users draw utility from the rankings, but the rankings also
determine the utility (e.g. exposure, revenue) for the item providers (e.g.
publishers, sellers, artists, studios). It has already been noted that
myopically optimizing utility to the users, as done by virtually all
learning-to-rank algorithms, can be unfair to the item providers. We,
therefore, present a learning-to-rank approach for explicitly enforcing
merit-based fairness guarantees to groups of items (e.g. articles by the same
publisher, tracks by the same artist). In particular, we propose a learning
algorithm that ensures notions of amortized group fairness, while
simultaneously learning the ranking function from implicit feedback data. The
algorithm takes the form of a controller that integrates unbiased estimators
for both fairness and utility, dynamically adapting both as more data becomes
available. In addition to its rigorous theoretical foundation and convergence
guarantees, we find empirically that the algorithm is highly practical and
robust.Comment: First two authors contributed equally. In Proceedings of the 43rd
International ACM SIGIR Conference on Research and Development in Information
Retrieval 202
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
This paper contributes improvements on both the effectiveness and efficiency
of Matrix Factorization (MF) methods for implicit feedback. We highlight two
critical issues of existing works. First, due to the large space of unobserved
feedback, most existing works resort to assign a uniform weight to the missing
data to reduce computational complexity. However, such a uniform assumption is
invalid in real-world settings. Second, most methods are also designed in an
offline setting and fail to keep up with the dynamic nature of online data. We
address the above two issues in learning MF models from implicit feedback. We
first propose to weight the missing data based on item popularity, which is
more effective and flexible than the uniform-weight assumption. However, such a
non-uniform weighting poses efficiency challenge in learning the model. To
address this, we specifically design a new learning algorithm based on the
element-wise Alternating Least Squares (eALS) technique, for efficiently
optimizing a MF model with variably-weighted missing data. We exploit this
efficiency to then seamlessly devise an incremental update strategy that
instantly refreshes a MF model given new feedback. Through comprehensive
experiments on two public datasets in both offline and online protocols, we
show that our eALS method consistently outperforms state-of-the-art implicit MF
methods. Our implementation is available at
https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection of
predefined ones is coined as document tagging in this work. Accurate tagging of
articles can benefit several downstream applications such as recommendation and
search. In this work, we propose a novel yet simple approach called DocTag2Vec
to accomplish this task. We substantially extend Word2Vec and Doc2Vec---two
popular models for learning distributed representation of words and documents.
In DocTag2Vec, we simultaneously learn the representation of words, documents,
and tags in a joint vector space during training, and employ the simple
-nearest neighbor search to predict tags for unseen documents. In contrast
to previous multi-label learning methods, DocTag2Vec directly deals with raw
text instead of provided feature vector, and in addition, enjoys advantages
like the learning of tag representation, and the ability of handling newly
created tags. To demonstrate the effectiveness of our approach, we conduct
experiments on several datasets and show promising results against
state-of-the-art methods.Comment: 10 page
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