Genre prediction to inform the recommendation process

Nevena Dragovic; Maria Soledad Pera

Genre prediction to inform the recommendation process

Research output: Contribution to journal › Conference article › Scientific › peer-review

Abstract

In this paper we present a time-based genre prediction strategy that can inform the book recommendation process. To explicitly consider time in predicting genres of interest, we rely on a popular time series forecasting model as well as reading patterns of each individual reader or group of readers (in case of libraries or publishing companies). Based on a conducted initial assessment using the Amazon dataset, we demonstrate our strategy outperforms its baseline counterpart.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	1688
Publication status	Published - 2016
Externally published	Yes
Event	10th ACM Conference on Recommender Systems, RecSys 2016 - MIT, Boston, MA, United States Duration: 15 Sep 2016 → 19 Sep 2016 https://recsys.acm.org/recsys16/

Keywords

ARIMA
Books
Genre
Prediction
Time Sequence

Cite this

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title = "Genre prediction to inform the recommendation process",

abstract = "In this paper we present a time-based genre prediction strategy that can inform the book recommendation process. To explicitly consider time in predicting genres of interest, we rely on a popular time series forecasting model as well as reading patterns of each individual reader or group of readers (in case of libraries or publishing companies). Based on a conducted initial assessment using the Amazon dataset, we demonstrate our strategy outperforms its baseline counterpart.",

keywords = "ARIMA, Books, Genre, Prediction, Time Sequence",

author = "Nevena Dragovic and Pera, {Maria Soledad}",

year = "2016",

language = "English",

volume = "1688",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

note = "10th ACM Conference on Recommender Systems, RecSys 2016 ; Conference date: 15-09-2016 Through 19-09-2016",

url = "https://recsys.acm.org/recsys16/",

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T1 - Genre prediction to inform the recommendation process

AU - Dragovic, Nevena

AU - Pera, Maria Soledad

PY - 2016

Y1 - 2016

N2 - In this paper we present a time-based genre prediction strategy that can inform the book recommendation process. To explicitly consider time in predicting genres of interest, we rely on a popular time series forecasting model as well as reading patterns of each individual reader or group of readers (in case of libraries or publishing companies). Based on a conducted initial assessment using the Amazon dataset, we demonstrate our strategy outperforms its baseline counterpart.

AB - In this paper we present a time-based genre prediction strategy that can inform the book recommendation process. To explicitly consider time in predicting genres of interest, we rely on a popular time series forecasting model as well as reading patterns of each individual reader or group of readers (in case of libraries or publishing companies). Based on a conducted initial assessment using the Amazon dataset, we demonstrate our strategy outperforms its baseline counterpart.

KW - ARIMA

KW - Books

KW - Genre

KW - Prediction

KW - Time Sequence

UR - http://www.scopus.com/inward/record.url?scp=84991071106&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84991071106

VL - 1688

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 10th ACM Conference on Recommender Systems, RecSys 2016

Y2 - 15 September 2016 through 19 September 2016

ER -

Genre prediction to inform the recommendation process

Abstract

Keywords

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