55,447 research outputs found
Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining
The notion of meta-mining has appeared recently and extends the traditional
meta-learning in two ways. First it does not learn meta-models that provide
support only for the learning algorithm selection task but ones that support
the whole data-mining process. In addition it abandons the so called black-box
approach to algorithm description followed in meta-learning. Now in addition to
the datasets, algorithms also have descriptors, workflows as well. For the
latter two these descriptions are semantic, describing properties of the
algorithms. With the availability of descriptors both for datasets and data
mining workflows the traditional modelling techniques followed in
meta-learning, typically based on classification and regression algorithms, are
no longer appropriate. Instead we are faced with a problem the nature of which
is much more similar to the problems that appear in recommendation systems. The
most important meta-mining requirements are that suggestions should use only
datasets and workflows descriptors and the cold-start problem, e.g. providing
workflow suggestions for new datasets.
In this paper we take a different view on the meta-mining modelling problem
and treat it as a recommender problem. In order to account for the meta-mining
specificities we derive a novel metric-based-learning recommender approach. Our
method learns two homogeneous metrics, one in the dataset and one in the
workflow space, and a heterogeneous one in the dataset-workflow space. All
learned metrics reflect similarities established from the dataset-workflow
preference matrix. We demonstrate our method on meta-mining over biological
(microarray datasets) problems. The application of our method is not limited to
the meta-mining problem, its formulations is general enough so that it can be
applied on problems with similar requirements
Real-Time Business Process Recommendations
Process Mining is a discipline focused on the analysis of the data logged by the execution of deployed business processes. Business process’ execution is not linear and might entail many decisions that affect the process execution. Decision Mining is a sub-field of Process mining, focused on finding and supporting these decision points. The decision criteria used in these decision points is often not explicit or optimized. Most research, techniques and algorithms in this area have been focused on providing off-line management support as means of explicitly representing implicit decisions. The solution proposed in this document presents a system that will provide the business actors a Best Next Action recommendation during the execution of business processes. To do so, it will be automatically identifying possible decision points, mine its data objects, apply probabilistic supervised learning algorithms and predict the best actions
When Social Influence Meets Item Inference
Research issues and data mining techniques for product recommendation and
viral marketing have been widely studied. Existing works on seed selection in
social networks do not take into account the effect of product recommendations
in e-commerce stores. In this paper, we investigate the seed selection problem
for viral marketing that considers both effects of social influence and item
inference (for product recommendation). We develop a new model, Social Item
Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we
formulate a seed selection problem, called Social Item Maximization Problem
(SIMP), and prove the hardness of SIMP. We design an efficient algorithm with
performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and
develop a new index structure, called SIG-index, to accelerate the computation
of diffusion process in HAG. Moreover, to construct realistic SIG models for
SIMP, we develop a statistical inference based framework to learn the weights
of hyperedges from data. Finally, we perform a comprehensive evaluation on our
proposals with various baselines. Experimental result validates our ideas and
demonstrates the effectiveness and efficiency of the proposed model and
algorithms over baselines.Comment: 12 page
A recommender system for process discovery
Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft
A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm
As the growing interest of web recommendation systems those are applied to
deliver customized data for their users, we started working on this system.
Generally the recommendation systems are divided into two major categories such
as collaborative recommendation system and content based recommendation system.
In case of collaborative recommen-dation systems, these try to seek out users
who share same tastes that of given user as well as recommends the websites
according to the liking given user. Whereas the content based recommendation
systems tries to recommend web sites similar to those web sites the user has
liked. In the recent research we found that the efficient technique based on
asso-ciation rule mining algorithm is proposed in order to solve the problem of
web page recommendation. Major problem of the same is that the web pages are
given equal importance. Here the importance of pages changes according to the
fre-quency of visiting the web page as well as amount of time user spends on
that page. Also recommendation of newly added web pages or the pages those are
not yet visited by users are not included in the recommendation set. To
over-come this problem, we have used the web usage log in the adaptive
association rule based web mining where the asso-ciation rules were applied to
personalization. This algorithm was purely based on the Apriori data mining
algorithm in order to generate the association rules. However this method also
suffers from some unavoidable drawbacks. In this paper we are presenting and
investigating the new approach based on weighted Association Rule Mining
Algorithm and text mining. This is improved algorithm which adds semantic
knowledge to the results, has more efficiency and hence gives better quality
and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table
Review on Service Recommendation System using Social User?s Rating Behaviors
The research communities of information retrieval, machine learning and data mining are recently started to paying attention towards Service recommendation systems. Traditional service recommendation algorithms are often based on batch machine learning methods which are having certain critical limitations, e.g., mostly systems are so costly also new user needs to pay the certain cost for new login, can?t capture the changes of user preferences over time. So that to overcome from that problem it is important to make service recommendation system more flexible for real world online applications where data arrives sequentially and user preferences may change randomly and dynamically. The proposed system present a new framework of online social recommendation on the basis of online graph regularized user preference learning (OGRPL), which incorporates both collaborative user-services relationship as well as service content features into an unified preference learning process. Also provide aggregated services in only one application (social networking) which increases user?s interest towards the services. Proposed system also provides security about subscribed services as well as documents/photos on online social network application. Proposed system utilizes services like Active Life, Beauty & Spas, Home Services, Hotels & Travel, Pets, Restaurants and Shopping
Service Recommendation System using Social User’s Rating Behaviors
The research communities of information retrieval, machine learning and data mining are recently started to paying attention towards Service recommendation systems. Traditional service recommendation algorithms are often based on batch machine learning methods which are having certain critical limitations, e.g., mostly systems are so costly also new user needs to pay the certain cost for new login, can’t capture the changes of user preferences over time. So that to overcome from that problem it is important to make service recommendation system more flexible for real world online applications where data arrives sequentially and user preferences may change randomly and dynamically. This system present a new website of online social recommendation on the basis of online graph regularized user preference learning (OGRPL), which incorporates both collaborative user-services relationship as well as service content features into an unified preference learning process. Also provide aggregated services in only one application (social networking) which increases user’s interest towards the services. This system also provides security about subscribed services as well as documents/photos on online social network application. This system will utilizes services like Education, adventure, Home Services, Hotels & Travel, Restaurants and Shopping
Performing Hybrid Recommendation in Intermodal Transportation – the FTMarket System’s Recommendation Module
Diverse recommendation techniques have been already proposed and encapsulated into several e-business applications, aiming to perform a more accurate evaluation of the existing information and accordingly augment the assistance provided to the users involved. This paper reports on the development and integration of a recommendation module in an agent-based transportation transactions management system. The module is built according to a novel hybrid recommendation technique, which combines the advantages of collaborative filtering and knowledge-based approaches. The proposed technique and supporting module assist customers in considering in detail alternative transportation transactions that satisfy their requests, as well as in evaluating completed transactions. The related services are invoked through a software agent that constructs the appropriate knowledge rules and performs a synthesis of the recommendation policy
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