COMPOUND CRITIQUING APPROACH FOR LAPTOP RECOMMENDATION IN CONVERSATIONAL RECOMMENDER SYSTEM USING COLLABORATIVE FILTERING

This study proposes a method for laptop recommendation in a conversational recommender system (CRS) by integrating collaborative filtering with the Apriori algorithm. The CRS interacts with users to help them find laptops that match their preferences, allowing them to provide feedback or critiques on the recommendations. This research emphasizes the use of compound critiques, which allow users to express preferences on multiple attributes at once, leading to more personalized recommendations. The Apriori algorithm identifies frequent itemsets from these critiques, which are then used to iteratively update recommendations. Evaluation results show that the High Support (HS) strategy, which focuses on commonly preferred features, produces more efficient recommendations, with a shorter average session duration of 38.01 seconds compared to the Low Support (LS) 41.30 seconds and Random (RAND) 50.49 seconds. This approach improves the recommendation process by better aligning with user preferences, which in turn improves interaction efficiency. @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536869121 1107305727 33554432 0 415 0;}@font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-469750017 -1073732485 9 0 511 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; text-align:center; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:EN-US;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt; mso-ascii-font-family:Calibri; mso-hansi-font-family:Calibri; mso-font-kerning:0pt; mso-ligatures:none; mso-ansi-language:EN-US;}div.WordSection1 {page:WordSection1;}

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Publication Details

Journal
INTEK Jurnal Penelitian
Published
2024-10-19
DOI
https://doi.org/10.31963/intek.v11i2.4893
Primary Topic
Recommender Systems and Techniques
Type
article
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article

COMPOUND CRITIQUING APPROACH FOR LAPTOP RECOMMENDATION IN CONVERSATIONAL RECOMMENDER SYSTEM USING COLLABORATIVE FILTERING

Z. K. A. Baizal, Ummu Husnul Khatimah
INTEK Jurnal Penelitian
Recommender Systems and Techniques
article

COMPOUND CRITIQUING APPROACH FOR LAPTOP RECOMMENDATION IN CONVERSATIONAL RECOMMENDER SYSTEM USING COLLABORATIVE FILTERING

Z. K. A. Baizal, Ummu Husnul Khatimah
article en

Abstract

This study proposes a method for laptop recommendation in a conversational recommender system (CRS) by integrating collaborative filtering with the Apriori algorithm. The CRS interacts with users to help them find laptops that match their preferences, allowing them to provide feedback or critiques on the recommendations. This research emphasizes the use of compound critiques, which allow users to express preferences on multiple attributes at once, leading to more personalized recommendations. The Apriori algorithm identifies frequent itemsets from these critiques, which are then used to iteratively update recommendations. Evaluation results show that the High Support (HS) strategy, which focuses on commonly preferred features, produces more efficient recommendations, with a shorter average session duration of 38.01 seconds compared to the Low Support (LS) 41.30 seconds and Random (RAND) 50.49 seconds. This approach improves the recommendation process by better aligning with user preferences, which in turn improves interaction efficiency. @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536869121 1107305727 33554432 0 415 0;}@font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-469750017 -1073732485 9 0 511 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; text-align:center; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:EN-US;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt; mso-ascii-font-family:Calibri; mso-hansi-font-family:Calibri; mso-font-kerning:0pt; mso-ligatures:none; mso-ansi-language:EN-US;}div.WordSection1 {page:WordSection1;}

INTEK Jurnal PenelitianVol. 11(2)
Telkom University (ID)
Quality Education
Openalex Percentile: Top 72%
Recommender Systems and Techniques
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