TMR: A Semantic Recommender System using Topic Maps on the Items’ Descriptions by Angel L. Garrido and Sergio Ilarri.
Abstract:
Recommendation systems have become increasingly popular these days. Their utility has been proved to filter and to suggest items archived at web sites to the users. Even though recommendation systems have been developed for the past two decades, existing recommenders are still inadequate to achieve their objectives and must be enhanced to generate appealing personalized recommendations e ectively. In this paper we present TMR, a context-independent tool based on topic maps that works with item’s descriptions and reviews to provide suitable recommendations to users. TMR takes advantage of lexical and semantic resources to infer users’ preferences and thus the recommender is not restricted by the syntactic constraints imposed on some existing recommenders. We have verifi ed the correctness of TMR using a popular benchmark dataset.
One of the more exciting aspects of this paper is the building of topic maps from free texts that are then used in the recommendation process.
I haven’t seen the generated topic maps (yet) but suspect that editing an existing topic map is far easier than creating one ab initio.