23,368 research outputs found
Competitive Intelligence and Internet Sources
In the Knowledge Age to maintain profitability and in some cases to remain in the market, companies must focus their actions in activities such as collecting, filtering, and disseminating information about market, about competitors and their actions. Those are part of Competitive Intelligence (CI) concept. In digital age, most of the information needed for CI projects is available on the web. This paper focuses on this field and presents a mix of directions that companies need to take into consideration in their CI projects in order to achieve the goals.competitive intelligence, web mining, information
A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs
Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed
A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs
Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
Customer churn prediction in telecom using machine learning and social network analysis in big data platform
Customer churn is a major problem and one of the most important concerns for
large companies. Due to the direct effect on the revenues of the companies,
especially in the telecom field, companies are seeking to develop means to
predict potential customer to churn. Therefore, finding factors that increase
customer churn is important to take necessary actions to reduce this churn. The
main contribution of our work is to develop a churn prediction model which
assists telecom operators to predict customers who are most likely subject to
churn. The model developed in this work uses machine learning techniques on big
data platform and builds a new way of features' engineering and selection. In
order to measure the performance of the model, the Area Under Curve (AUC)
standard measure is adopted, and the AUC value obtained is 93.3%. Another main
contribution is to use customer social network in the prediction model by
extracting Social Network Analysis (SNA) features. The use of SNA enhanced the
performance of the model from 84 to 93.3% against AUC standard. The model was
prepared and tested through Spark environment by working on a large dataset
created by transforming big raw data provided by SyriaTel telecom company. The
dataset contained all customers' information over 9 months, and was used to
train, test, and evaluate the system at SyriaTel. The model experimented four
algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM"
and Extreme Gradient Boosting "XGBOOST". However, the best results were
obtained by applying XGBOOST algorithm. This algorithm was used for
classification in this churn predictive model.Comment: 24 pages, 14 figures. PDF https://rdcu.be/budK
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