NDLI: Modeling Individual Users' Responsiveness to Maximize Recommendation Impact

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Modeling Individual Users' Responsiveness to Maximize Recommendation Impact

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Modeling Individual Users' Responsiveness to Maximize Recommendation Impact

Content Provider	ACM Digital Library
Author	Sato, Masahiro Sonoda, Takashi Izumo, Hidetaka
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.
Starting Page	259
Ending Page	267
Page Count	9
File Format	PDF
ISBN	9781450343688
DOI	10.1145/2930238.2930259
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Grocery shopping Cold-start problem Profit maximization Matrix factorization Personality Purchase prediction
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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Modeling Individual Users' Responsiveness to Maximize Recommendation Impact

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