Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems

Recommender systems have taken over user’s choice to choose the items/services they want from online markets, where lots of merchandise is traded. Collaborative filtering-based recommender systems uses user opinions and preferences. Determination of commonly used attributes that influence preferences used for prediction and subsequent recommendation of unknown or new items to users is a significant objective while developing recommender engines.  In conventional systems, study of user behavior to know their dis/like over items would be carried-out. In this paper, presents feature selection methods to mine such preferences through selection of high influencing attributes of the items. In machine learning, feature selection is used as a data pre-processing method but extended its use on this work to achieve two objectives; removal of redundant, uninformative features and for selecting formative, relevant features based on the response variable. The latter objective, was suggested to identify and determine the frequent and shared features that would be preferred mostly by marketplace online users as they express their preferences. The dataset used for experimentation and determination was synthetic dataset.  The Jupyter Notebook™ using python was used to run the experiments. Results showed that given a number of formative features, there were those selected, with high influence to the response variable. Evidence showed that different feature selection methods resulted with different feature scores, and intrinsic method had the best overall results with 85% model accuracy. Selected features were used as frequently preferred attributes that influence users’ preferences

Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems

Recommender systems have taken over user’s choice to choose the items/services they want from online markets, where lots of merchandise is traded. Collaborative filtering-based recommender systems uses user opinions and preferences. Determination of commonly used attributes that influence preferences used for prediction and subsequent recommendation of unknown or new items to users is a significant objective while developing recommender engines.  In conventional systems, study of user behavior to know their dis/like over items would be carried-out. In this paper, presents feature selection methods to mine such preferences through selection of high influencing attributes of the items. In machine learning, feature selection is used as a data pre-processing method but extended its use on this work to achieve two objectives; removal of redundant, uninformative features and for selecting formative, relevant features based on the response variable. The latter objective, was suggested to identify and determine the frequent and shared features that would be preferred mostly by marketplace online users as they express their preferences. The dataset used for experimentation and determination was synthetic dataset.  The Jupyter Notebook™ using python was used to run the experiments. Results showed that given a number of formative features, there were those selected, with high influence to the response variable. Evidence showed that different feature selection methods resulted with different feature scores, and intrinsic method had the best overall results with 85% model accuracy. Selected features were used as frequently preferred attributes that influence users’ preferences

Solución de inteligencia de negocios basada en técnicas de minería de datos, para apoyar la toma de decisiones, en la Gerencia Regional de Agricultura-Lambayeque

La relación que exime los procesos tecnológicos en el universo actualmente es de mucha transcendencia para el individuo y su colectividad. En corto tiempo el ser humano se ha profundizado en el uso de la tecnología para beneficio de sus ocupaciones habituales como investigativas, industriales o comerciales. Esta evolución tecnológica sirve de soporte a las operaciones y a la gestión de las instituciones, por esta relevancia en el progreso evolutivo, para apoyar a la Dirección Ejecutiva de Información Agraria de la Gerencia Regional de Agricultura de Lambayeque, propongo, el desarrollo de una solución de Inteligencia de negocios basado en técnicas de minería de datos como herramienta para potenciar sus actividades de gestión de información. Resumiendo, el trabajo consistió, primero, en estudiar las principales metodologías de desarrollo existentes: este caso se decidió llevar a cabo un hibrido de metodologías Ralph Kimball con el objeto de implementar almacenes de datos y CRISP-DM para el procesamiento electrónico de datos, elección y aplicación de métodos predictivos. La aplicación de la dimensión sistemática de alto nivel nos permitirá medir el hecho-siembra y hecho-cosecha tales como: valle, tiempo, tipo de consumo, cultivo, campaña agrícola. Los resultados demuestran que el modelo propuesto es de Regresión lineal con ajuste estacional, puesto que ostentó un error cuadrático menor a comparación con el enfoque Naive estacional y el modelo ARIMA (0,0,1) (0,1,0) [12], además de tener un valor MAPE ligeramente superior al valor obtenido por los demás

A survey on data mining techniques in recommender systems

Recommender systems have been regarded as gaining a more significant role with the emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF predicts the interests of an active user based on the opinions of users with similar interests. To extract information on the preference of users for a set of items and evaluate the performance of the recommender system’s techniques and algorithms, a critical analysis can be conducted. This study therefore employs a critical analysis on 131 articles in CF area from 36 journals published between the years 2010 and 2016. This analysis seems to be the exclusive survey which supports and motivates the community of researchers and practitioners. It is done by using the applications of users’ activities and intelligence computing and data mining techniques on CF recommendation systems. In addition, it provides a classification of the literature on academic database according to the benchmark recommendation databases, two users’ feedbacks (explicit and implicit feedbacks) which reflect their activities and categories of intelligence computing and data mining techniques. Eventually, this study provides a road map to guide future direction on recommender systems research and facilitates the accumulated and derived knowledge on the application of intelligence computing and data mining techniques in CF recommendation systems