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Article: Asymmetric response aggregation heuristics for rating prediction and recommendation

TitleAsymmetric response aggregation heuristics for rating prediction and recommendation
Authors
KeywordsCollaborative filtering
Response
Positive suggestions
Negative suggestions
Linear regression method
Issue Date2020
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-669X
Citation
Applied Intelligence, 2020, v. 50 n. 5, p. 1416-1436 How to Cite?
AbstractUser-based collaborative filtering is widely used in recommendation systems, which normally comprises three steps: (1) finding the nearest conceptual neighbors, (2) aggregating the neighbors’ ratings to predict the ratings of unrated items, and (3) generating recommendations based on the prediction. Existing algorithms mainly focus on steps 1 and 3 but neglect subtle treatments of aggregating neighbors’ suggestions in step 2. Based on the discovery of psychology that (i) users’ responses to positive and negative suggestions are different, and (ii) users may respond differently from one another, this paper proposes a Personal Asymmetry Response-based Suggestions Aggregation (PARSA) algorithm, which first uses a linear regression method to learn each user’s response to negative/positive suggestions from neighbors and then uses a gradient descent algorithm for optimizing them. In addition, this paper designs an Identical Asymmetry Response-based Suggestions Aggregation (IARSA) baseline algorithm, which assumes that all the users’ responses to suggestions are identical as references to verify the key contribution of the heuristics employed in our PARSA algorithm that user may responses differently to positive and negative suggestions. Three sets of experiments are designed and implemented over two real-life datasets (i.e., Eachmovie and Netflix) to evaluate the performance of our algorithms. Further, in order to eliminate the influence of different similarity measures, this paper selects three kinds of similarity measures to discover neighbors. Experimental results demonstrate that most people indeed pay more attention to negative suggestions and our algorithms achieve better prediction and recommendation performances than the compared algorithms under various similarity measures.
Persistent Identifierhttp://hdl.handle.net/10722/286375
ISSN
2019 Impact Factor: 3.325
2015 SCImago Journal Rankings: 0.777
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJi, S-
dc.contributor.authorYang, W-
dc.contributor.authorGuo, S-
dc.contributor.authorChiu, DKW-
dc.contributor.authorZhang, C-
dc.contributor.authorYuan, X-
dc.date.accessioned2020-08-31T07:02:58Z-
dc.date.available2020-08-31T07:02:58Z-
dc.date.issued2020-
dc.identifier.citationApplied Intelligence, 2020, v. 50 n. 5, p. 1416-1436-
dc.identifier.issn0924-669X-
dc.identifier.urihttp://hdl.handle.net/10722/286375-
dc.description.abstractUser-based collaborative filtering is widely used in recommendation systems, which normally comprises three steps: (1) finding the nearest conceptual neighbors, (2) aggregating the neighbors’ ratings to predict the ratings of unrated items, and (3) generating recommendations based on the prediction. Existing algorithms mainly focus on steps 1 and 3 but neglect subtle treatments of aggregating neighbors’ suggestions in step 2. Based on the discovery of psychology that (i) users’ responses to positive and negative suggestions are different, and (ii) users may respond differently from one another, this paper proposes a Personal Asymmetry Response-based Suggestions Aggregation (PARSA) algorithm, which first uses a linear regression method to learn each user’s response to negative/positive suggestions from neighbors and then uses a gradient descent algorithm for optimizing them. In addition, this paper designs an Identical Asymmetry Response-based Suggestions Aggregation (IARSA) baseline algorithm, which assumes that all the users’ responses to suggestions are identical as references to verify the key contribution of the heuristics employed in our PARSA algorithm that user may responses differently to positive and negative suggestions. Three sets of experiments are designed and implemented over two real-life datasets (i.e., Eachmovie and Netflix) to evaluate the performance of our algorithms. Further, in order to eliminate the influence of different similarity measures, this paper selects three kinds of similarity measures to discover neighbors. Experimental results demonstrate that most people indeed pay more attention to negative suggestions and our algorithms achieve better prediction and recommendation performances than the compared algorithms under various similarity measures.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-669X-
dc.relation.ispartofApplied Intelligence-
dc.subjectCollaborative filtering-
dc.subjectResponse-
dc.subjectPositive suggestions-
dc.subjectNegative suggestions-
dc.subjectLinear regression method-
dc.titleAsymmetric response aggregation heuristics for rating prediction and recommendation-
dc.typeArticle-
dc.identifier.emailChiu, DKW: dchiu88@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10489-019-01594-2-
dc.identifier.scopuseid_2-s2.0-85078244487-
dc.identifier.hkuros313602-
dc.identifier.volume50-
dc.identifier.issue5-
dc.identifier.spage1416-
dc.identifier.epage1436-
dc.identifier.isiWOS:000524237100005-
dc.publisher.placeUnited States-

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