Bedrijfseconomie/Data Science

Understanding the subscribing behaviour of customers during an economic crisis

Tue, 03 Sep 2019 00:00:01 GMT

Bundhun, M. (Manish)

Prediction of Residual Value for Lease Cars Using Machine Learning Techniques

Mon, 09 Sep 2019 00:00:01 GMT

Gulati, M. (Mehak)

The influence of customer demand maturity and natural ordering in the next best offer making applied to the financial product and service market in Spain

Thu, 19 Sep 2019 00:00:01 GMT

Dekkers, S.C.W.

Analysing and Integrating Numerical Ratings and Textual Reviews with Natural Language Processing

Tue, 24 Sep 2019 00:00:01 GMT

Madacsi, A. (Adam)

A new approach to analyze price sensitivity across countries

Thu, 26 Sep 2019 00:00:01 GMT

Velzen, Q.A. van

Features extraction from employee reviews mining and the content-based job satisfaction prediction

Mon, 30 Sep 2019 00:00:01 GMT

Zou, X. (Xueshan)

In this thesis, the employees' job satisfaction was predicting by detecting the latent dimensions that hide in the employees’ reviews. The dataset of employees’ job satisfaction reviews was scrapped from the public website Glassdoor. The topic model Latent Dirichlet Allocation (LDA) was utilized to extract the informative hidden dimensions in the reviews. The topic probability obtained from LDA was taken as the predictor features (variables), which are used for predicting job satisfaction ratings through regression models. The results show that these LDA features can significantly improve the accuracy of prediction. In addition, it also provides information about what topics significantly influence job satisfaction. Based on that, some meaningful suggestions can be offered to these companies on how to increase their employees' job satisfaction, so as to decrease employee turnover.

Text Mining Comments: Application on Consumer Response Towards Zero Waste YouTube content

Mon, 30 Sep 2019 00:00:01 GMT

Suwarso, K.B. (Katharine)

The effects of demographic information in matrix factorization recommender systems on accuracy and serendipity for varying levels of user feedback

Mon, 30 Sep 2019 00:00:01 GMT

Raaij, M.M.A. van (Maarten)

In this thesis we investigate the influence of demographic information in a matrix factorization recommender systems on the accuracy and serendipity of its recommendations. In particular we investigate possible disparate effects for different levels of user feedback available in addition to the global effects on all users. In line with previous research we have found that adding demographics on average improves accuracy. The performance advantage does seem to diminish as more feedback is available per user, yet there is no evidence to belief that the demographics will ever become noise to the model. In terms of serendipity the consequences of demographic information are twofold. On one hand they lead to less novel recommendations on average. On the other hand demographics lead to recommendations with higher unexpectedness given the user’s context. The performance differences are however small and again seem to diminish for higher levels of feedback available per user. In general it is therefore beneficial to include demographics in a matrix factorization recommender as it alleviates the cold-start problem, while doing little harm to serendipity. Only when the ultimate goal is selling the most novel items it would be advised to omit the user’s demographic background for the recommendation.

Weighted Linear Ridge Regression as an Approximation of Kernel Ridge Regression Kernels

Tue, 10 Dec 2019 00:00:01 GMT

Huang, L.

Evaluating Machine Learning Methods for House Price Prediction

Thu, 12 Dec 2019 00:00:01 GMT

Heybeli, B. (Binal)

Personalized Recommendations With Matrix Factorization Models That Take Into Account User Information

Sat, 14 Dec 2019 00:00:01 GMT

Geffen, F.M. van (Freek)