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Thanks for the help:

The effect of content-based recommendations on advertisement

By:

Joost Dorgelo

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Content

› Introduction

› Recommendations

› Music

› Online advertising

› Conceptual model

› Method

› Result

› Discussion

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First look

›Research Question: what are the effects of

content-based recommendation system on the amount of

banner impressions and the click trough rate?

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Recommendations

› Shift from analog to digital music storage. › Increased choice.

▪ More difficult to find music of the preferred type (Kaminskas & Ricci,

2012; Chung, Rust & Wedel, 2007).

› Consumers get help with finding their preferred music

(Hennig-Thurau, Marchand & Marx, 2012; Fleder & Hosanagar, 2009; Kaminskas & Ricci, 2012).

› The recommendation systems improve sales numbers (Fleder & Hosanagar, 2009).

▪ Not by increasing the amount of options, but by increasing the

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Effect on advertisement

› Money is earned indirectly.

- by targeting the user with advertisement.

› Many online platforms do not rely on sales, but are dependent on advertisements.

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Why do we use technology in the first place

› TAM Model by Davis (1986).

▪ Usefulness & efficiency.

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Music type

› Genre classification

(Silver, Lee & Childress, 2016).

-

Grouping of the industry,

-

Listening to the same type of music.

› 25 music genres -> 6 music types

(Shäfer & Sedlmeier, 2009).

-

Electronic, Sophisticated, Rock, Pop, Folk, Rap.

-

These 6 music types are related to the fulfillment

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Recommendations based on music type

› Recommendations based on music type, therefore

believed increase the usability of the platform for the

user. This is a main driver for the use of the platform

› H1: recommendations based on music preference are more likely used than generic

recommendations.

› H2: recommendations based on the music preference will increase the amount of

visits to the website.

› H3: recommendations based on the music preference will increase the duration of

the visits of the website.

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Individual differences

› Different personality’s have different music preferences (Rentfrow & Gosling, 2003).

› H4: People with a preference for Rap, Pop or Electronic music, are more likely to

use recommendations based on music type.

› Rap and Electronic are linked to agreeableness, making them more likely to use the recommendations.

› Pop is negatively linked to openness to new experience.

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Online advertising

› For a publisher, every visit is a product sold (Chen & Stallaert, 2014)

- H2: recommendations based on the music preference will increase the amount

of visits to the website.

Click trough rate

- Increased duration of exposure towards the banner

advertisement, increases the probability to click on the banner

(Ghose & Todri, 2015).

› H5: Visit duration has a positive effect on click trough rate.

› H6: Duration of the website visits mediate the effect that recommendations based

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Classification & Clustering

› Music streaming service Songa.

› 301 randomly selected users.

› Artist classified based on music type.

› Recommend music based on their preference.

› Test and control group.

›K-means++.

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Recommendations

› Every user gets five

recommendations

based on their music

preference.

› Placed in the

recommendations

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Classification

› No Sophisticated and Folk preference.

› Dutch folk music and Pop/Rock preference.

Music type Amount of songs % % cumulative

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Clustering

›DB index of 33 at six clusters was the lowest score.

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Hypothesis testing

› H1: Music type recommendations increases recommender use.

- Binary logistic regression.

- Not significant

› H2: Recommender use increases the amount of visits.

- Linear regression.

- Not significant

› H3: Recommender use increases the visit duration.

- Linear regression.

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Hypothesis testing

› H4: Electronic, Rap or Pop are more likely to use the recommendations based on music type.

- Binary logistic regression.

- Having a preference for Electronic, Rap or Pop music, makes a user

more likely to use the recommendations based on music type. › Further analysis of Pop preference

- Majority of the uses was caused by Pop listeners.

- Further analysis turned out that only Pop listeners are more likely to

use the recommendations.

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Hypothesis testing

› H5: visit duration increases the click trough rate.

- Linear regression.

- Not significant

› H6: the effect of recommender use on click trough rate is mediated by visit duration.

- Non parametric bootstrap mediation analysis (Preacher & Hayes,

2004).

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Discussion

› Based on the collected data:

- The use of content-based recommendations based on music

type is discouraged, if maximizing advertising revenue is the goal.

- Pop listeners are more likely to use recommendations systems

that are based on music type.

- Pop and Rock music are the most popular genres.

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Additional findings

Electronic listeners have shorter session (marginally significant).

- 27 minutes lower than the average. - Relation with exercise.

- This confirms the importance of context.

› Increased amount of visits make users more likely to adopt the recommendations (marginally significant).

- Increasing odds of 5% per visit. - Relation with boredom.

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Limitation & Future Research

› Short observation window.

› Only 1 set of recommendation. › Dutch Folk music.

› Only popular music was classified.

› Different types of recommender systems:

- Collaborative, - Context.

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Thank you for your attention!

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Literature

Anderson, C. 2009. FREE: The future of a Radical Price. New York: Hyperion Books.

Chen, J., & Stallaert, J. (2014). An economic analysis of online advertising using behavioral targeting. Mis Quarterly, 38(2), 429-449.

Chung, T. S., Rust, R. T., & Wedel, M. (2009). My mobile music: An adaptive personalization system for digital audio players. Marketing Science, 28(1), 52-68.

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.

Fleder, D., & Hosanagar, K. (2009). Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity. Management science, 55(5), 697-712.

Häubl, G., & Murray, K. B. (2001). Recommending or persuading?: the impact of a shopping agent's algorithm on user behavior. In Proceedings of the 3rd ACM conference on Electronic Commerce (pp. 163-170). ACM.

Hemming, J. (2013). Is there a peak in popular music preference at a certain song-specific age? A replication of Holbrook & Schindler’s 1989 study. Musicae Scientiae, 17(3), 293-304.

Hennig-Thurau, T., Marchand, A., & Marx, P. (2012). Can automated group recommender systems help consumers make better choices?. Journal of Marketing, 76(5), 89-109.

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Literature

Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior research methods, 36(4), 717-731.

Rentfrow, P. J., & Gosling, S. D. (2003). The do re mi's of everyday life: the structure and personality correlates of music preferences. Journal of personality and social psychology, 84(6), 1236.

Schäfer, T., & Sedlmeier, P. (2009). From the functions of music to music preference. Psychology of Music, 37(3), 279-300.

Silver, D., Lee, M., & Childress, C. C. (2016). Genre Complexes in Popular Music. PloS one, 11(5).

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