USING META-DATA FROM FREE-TEXT USER-GENERATED CONTENT TO IMPROVE PERSONALIZED RECOMMENDATION BY REDUCING SPARSITY

Abstract

RECOMMENDER SYSTEMS HAVE BECOME INCREASINGLY ESSENTIAL IN MANY DOMAINS FOR ALLEVIATING THE ?INFORMATION OVERLOAD? PROBLEM, BUT EXISTING RECOMMENDATION TECHNIQUES SUFFER FROM THE SPARSITY PROBLEM DUE TO INSUFFICIENT INPUT DATA. IN THIS THESIS, WE AIM AT EXTRACTING AND INCORPORATING META-DATA FROM FREE-TEXT USER-GENERATED CONTENT (UGC) TO LESSEN THE EFFECTS OF SPARSITY AND THEREFORE IMPROVE THE QUALITY OF RECOMMENDATION. WE ACHIEVE THIS GOAL BY CONDUCTING THREE DIFFERENT STUDIES, EACH OF WHICH PROPOSES A RECOMMENDATION SOLUTION THAT INCORPORATES UGC FROM DIFFERENT PERSPECTIVES, AND ADDRESSES SPECIFIC PROBLEMS INTRODUCED BY DATA SPARSITY IN DIFFERENT CONTEXTS. IN PARTICULAR, IN STUDY ONE, WE SHOW THAT ADJECTIVE FEATURES EMBEDDED IN USER REVIEWS ARE USEFUL FOR CHARACTERIZING MOVIE FEATURES AS WELL AS USER TASTES. IN STUDY TWO, WE SHOW THAT CRITIC REVIEWS OF THE ITEMS CAN BE USED TO BOOST NEW ITEM RECOMMENDATION. STUDY THREE FOCUSES ON EXTRACTING FUNCTIONAL ASPECTS FROM USER REVIEWS FOR MOB