Data Mining Techniques and Preference Learning in Recommender Systems

Abstract

The importance of implementing recommender systems has significantly increased during the last decade. The majority of available recommender systems do not offer clients the ability to make selections based on their choices or desires. This has motivated the development of a web based recommender system in order to recommend products to users and customers. The new system is an extension of an online application previously developed for online shopping under constraints and preferences. In this work, the system is enhanced by introducing a learning component to learn user preferences and suggests products based on them. More precisely, the new component learns from other customers' preferences and makes a set of recommendations using data mining techiques including classification, association rules and cluster analysis techniques. The results of experimental tests, conducted to evaluate the performance of this component when compiling a list of recommendations, are very promising.

Necessity and Preference Mining from Text Reviews: A Non-bipolar assessment of Text Reviews

“…The AprioriTid algorithm and association rules are used to find frequent item sets and preferences to recommend products to users. This work is an extension of online application developed previously for online shopping (7) . The proposed work is for both mining and classification of hotel reviews as preference and necessity using modals and thus its differs from works in (1)(2)(3)(4)(5)(6)(7) .…”

“…This work is an extension of online application developed previously for online shopping (7) . The proposed work is for both mining and classification of hotel reviews as preference and necessity using modals and thus its differs from works in (1)(2)(3)(4)(5)(6)(7) . A novel approach for mining preferences from user log data based on the concept of strict partial order preferences was presented.…”