Matches in SemOpenAlex for { <https://semopenalex.org/work/W2463291531> ?p ?o ?g. }
Showing items 1 to 64 of
64
with 100 items per page.
- W2463291531 abstract "Recommender systems provide personalized information based on a user's preferences. Differences in preferences among users are estimated from past records such as click logs or purchase logs. Recommender systems typically assume that users will respond to recommendations, provided that their favorite items are correctly selected. However, the responsiveness to recommendations depends on the type of users; while some users might be easily persuaded to take action, others might be more hesitant. In this paper, we propose a purchase prediction model that incorporates the differences in the responsiveness. We derived the individual users' responsiveness from a combination of purchase logs and recommendation logs. Improvement in the accuracy of purchase prediction was verified using a grocery shopping dataset. Another relatively unexplored yet important objective of recommender algorithms is to maximize recommendation impact, which is defined as the increase in purchase probability through recommendations. The impact of recommendations by our model exceeded that of a conventional model that ignores individual users' responsiveness. These results demonstrate the importance of modeling the responsiveness of individual users. In cases where recommendation logs are insufficient, the responsiveness needs to be estimated from other sources. Consequently, we investigated the correlation of the responsiveness with user attributes and item attributes. The estimates of the responsiveness from the correlated attributes outperformed the mean estimates. Furthermore, the recommendation impact of the model estimated from the correlated attributes was almost comparable to that of the model estimated from recommendation logs. These findings can help overcome the cold-start problem of inadequate recommendation logs. Our study presents a new direction in the field of personalization based on the responsiveness to recommendations." @default.
- W2463291531 created "2016-07-22" @default.
- W2463291531 creator A5026575535 @default.
- W2463291531 creator A5043127943 @default.
- W2463291531 creator A5065477835 @default.
- W2463291531 date "2016-07-13" @default.
- W2463291531 modified "2023-10-16" @default.
- W2463291531 title "Modeling Individual Users' Responsiveness to Maximize Recommendation Impact" @default.
- W2463291531 cites W1990184998 @default.
- W2463291531 cites W1993541285 @default.
- W2463291531 cites W2016986376 @default.
- W2463291531 cites W2017360334 @default.
- W2463291531 cites W2028595520 @default.
- W2463291531 cites W2033213271 @default.
- W2463291531 cites W2049670925 @default.
- W2463291531 cites W2054141820 @default.
- W2463291531 cites W2082831169 @default.
- W2463291531 cites W2100719534 @default.
- W2463291531 cites W2101409192 @default.
- W2463291531 cites W2123310993 @default.
- W2463291531 cites W2124187902 @default.
- W2463291531 cites W2158515176 @default.
- W2463291531 cites W2171960770 @default.
- W2463291531 cites W2173626428 @default.
- W2463291531 cites W2203455183 @default.
- W2463291531 cites W2213191543 @default.
- W2463291531 cites W4249279174 @default.
- W2463291531 doi "https://doi.org/10.1145/2930238.2930259" @default.
- W2463291531 hasPublicationYear "2016" @default.
- W2463291531 type Work @default.
- W2463291531 sameAs 2463291531 @default.
- W2463291531 citedByCount "12" @default.
- W2463291531 countsByYear W24632915312017 @default.
- W2463291531 countsByYear W24632915312019 @default.
- W2463291531 countsByYear W24632915312020 @default.
- W2463291531 countsByYear W24632915312021 @default.
- W2463291531 countsByYear W24632915312023 @default.
- W2463291531 crossrefType "proceedings-article" @default.
- W2463291531 hasAuthorship W2463291531A5026575535 @default.
- W2463291531 hasAuthorship W2463291531A5043127943 @default.
- W2463291531 hasAuthorship W2463291531A5065477835 @default.
- W2463291531 hasConcept C23123220 @default.
- W2463291531 hasConcept C41008148 @default.
- W2463291531 hasConcept C557471498 @default.
- W2463291531 hasConceptScore W2463291531C23123220 @default.
- W2463291531 hasConceptScore W2463291531C41008148 @default.
- W2463291531 hasConceptScore W2463291531C557471498 @default.
- W2463291531 hasLocation W24632915311 @default.
- W2463291531 hasOpenAccess W2463291531 @default.
- W2463291531 hasPrimaryLocation W24632915311 @default.
- W2463291531 hasRelatedWork W2045871438 @default.
- W2463291531 hasRelatedWork W2122731942 @default.
- W2463291531 hasRelatedWork W2348159088 @default.
- W2463291531 hasRelatedWork W2350747448 @default.
- W2463291531 hasRelatedWork W2368095327 @default.
- W2463291531 hasRelatedWork W2402445420 @default.
- W2463291531 hasRelatedWork W2499363748 @default.
- W2463291531 hasRelatedWork W2514849893 @default.
- W2463291531 hasRelatedWork W2809363009 @default.
- W2463291531 hasRelatedWork W2968745142 @default.
- W2463291531 isParatext "false" @default.
- W2463291531 isRetracted "false" @default.
- W2463291531 magId "2463291531" @default.
- W2463291531 workType "article" @default.