000 02301nam a22002897a 4500
003 KOHA
005 20230419105202.0
008 230302d2022 cy ||||| m||| 00| 0 eng d
040 _aCY-NiCIU
_beng
_cCY-NiCIU
_erda
041 _aeng
090 _aYL 2679
_bR29 2022
100 1 _aRawahi, Sanaa Saeed Musallam Al
245 1 0 _aDAILY CONSUMER PURCHASE SUGGESTED LIST BY RECOMMENDED SYSTEM /
_cSANAA SAEED MUSALLAM AL RAWAHI; SUPERVISOR: PROF. DR. ERBUĞ ÇELEBİ
264 _c2022
300 _a78 sheets;
_c31 cm.
_eIncludes CD
336 _2rdacontent
_atext
_btxt
337 _2rdamedia
_aunmediated
_bn
338 _2rdacarrier
_avolume
_bnc
500 _aIncludes bibliography (sheets 75-78)
502 _aThesis (MSc) Cyprus International University. Institute of Graduate Studies and Research Information Technologies Department
504 _aIncludes bibliography (sheets 75-78)
520 _aABSTRACT The aim of this study is to use algorithms to build a recommender system that can generate a suggestion for consumer shopping list. We analyzed the effectiveness of a recommender system using three different methods collaborative filtering (CF), Content-based filtering (C-B) and Hybrid). We investigate and test each approach of the recommended system. Next we have evaluated the accuracy of the system using Recall@5 scored 79% for popularity and 67% for CF. Recall@10 scored for popularity and CF 91% and 79% respectively, Precision@5 scored 77% and 75% for Hybrid and CF approaches Precision@10 scored 88% for Hybrid and 82% for CF. The harmonic evaluation for both Recall and Precision F1@10 scored 88% and 80% for CF and Hybrid approach. Finally, we observe that the content-based scored the lowest which is we conclude that it is superior to CF for typical consumer purchases. We observed that hybrid approach provides the best results for making recommendations to brand-new users with no prior information. Keywords: Collaborative Filtering, Content-Based Filtering, Hybrid Filtering, Precision, Recall, Recommendation System
650 0 _aRecommender systems (Information filtering)
_vDissertations, Academic
700 1 _aÇelebi, Erbuğ
_esupervisor
942 _2ddc
_cTS
999 _c289832
_d289832