15,232 research outputs found
On the Impact of Entity Linking in Microblog Real-Time Filtering
Microblogging is a model of content sharing in which the temporal locality of
posts with respect to important events, either of foreseeable or unforeseeable
nature, makes applica- tions of real-time filtering of great practical
interest. We propose the use of Entity Linking (EL) in order to improve the
retrieval effectiveness, by enriching the representation of microblog posts and
filtering queries. EL is the process of recognizing in an unstructured text the
mention of relevant entities described in a knowledge base. EL of short pieces
of text is a difficult task, but it is also a scenario in which the information
EL adds to the text can have a substantial impact on the retrieval process. We
implement a start-of-the-art filtering method, based on the best systems from
the TREC Microblog track realtime adhoc retrieval and filtering tasks , and
extend it with a Wikipedia-based EL method. Results show that the use of EL
significantly improves over non-EL based versions of the filtering methods.Comment: 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 -
17, 201
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
Algorithms and Architecture for Real-time Recommendations at News UK
Recommendation systems are recognised as being hugely important in industry,
and the area is now well understood. At News UK, there is a requirement to be
able to quickly generate recommendations for users on news items as they are
published. However, little has been published about systems that can generate
recommendations in response to changes in recommendable items and user
behaviour in a very short space of time. In this paper we describe a new
algorithm for updating collaborative filtering models incrementally, and
demonstrate its effectiveness on clickstream data from The Times. We also
describe the architecture that allows recommendations to be generated on the
fly, and how we have made each component scalable. The system is currently
being used in production at News UK.Comment: Accepted for presentation at AI-2017 Thirty-seventh SGAI
International Conference on Artificial Intelligence. Cambridge, England 12-14
December 201
Evaluation of recommender systems in streaming environments
Evaluation of recommender systems is typically done with finite datasets.
This means that conventional evaluation methodologies are only applicable in
offline experiments, where data and models are stationary. However, in real
world systems, user feedback is continuously generated, at unpredictable rates.
Given this setting, one important issue is how to evaluate algorithms in such a
streaming data environment. In this paper we propose a prequential evaluation
protocol for recommender systems, suitable for streaming data environments, but
also applicable in stationary settings. Using this protocol we are able to
monitor the evolution of algorithms' accuracy over time. Furthermore, we are
able to perform reliable comparative assessments of algorithms by computing
significance tests over a sliding window. We argue that besides being suitable
for streaming data, prequential evaluation allows the detection of phenomena
that would otherwise remain unnoticed in the evaluation of both offline and
online recommender systems.Comment: Workshop on 'Recommender Systems Evaluation: Dimensions and Design'
(REDD 2014), held in conjunction with RecSys 2014. October 10, 2014, Silicon
Valley, United State
Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis
Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer’s generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers’ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising
A Faster Algorithm to Build New Users Similarity List in Neighbourhood-based Collaborative Filtering
Neighbourhood-based Collaborative Filtering (CF) has been applied in the
industry for several decades, because of the easy implementation and high
recommendation accuracy. As the core of neighbourhood-based CF, the task of
dynamically maintaining users' similarity list is challenged by cold-start
problem and scalability problem. Recently, several methods are presented on
solving the two problems. However, these methods applied an algorithm
to compute the similarity list in a special case, where the new users, with
enough recommendation data, have the same rating list. To address the problem
of large computational cost caused by the special case, we design a faster
() algorithm, TwinSearch Algorithm, to avoid computing and
sorting the similarity list for the new users repeatedly to save the
computational resources. Both theoretical and experimental results show that
the TwinSearch Algorithm achieves better running time than the traditional
method
Intelligent Product Brokering for E-Commerce: An Incremental Approach to Unaccounted Attribute Detection
This research concentrates on designing generic product-brokering agent to understand user preference towards a product category and recommends a list of products to the user according to the preference captured by the agent. The proposed solution is able to detect both quantifiable and non-quantifiable attributes through a user feedback system. Unlike previous approaches, this research allows the detection of unaccounted attributes that are not within the ontology of the system. No tedious change of the algorithm, database, or ontology is required when a new product attribute is introduced. This approach only requires the attribute to be within the description field of the product. The system analyzes the general product descriptions field and creates a list of candidate attributes affecting the user’s preference. A genetic algorithm verifies these candidate attributes and excess attributes are identified and filtered off. A prototype has been created and our results show positive results in the detection of unaccounted attributes affecting a user
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