ExcUseMe

Aharon, Michal; Anava, Oren; Avigdor-Elgrabli, Noa; Drachsler-Cohen, Dana; Golan, Shahar; Somekh, Oren

doi:10.1145/2792838.2800183

Cited by 20 publications

(1 citation statement)

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“…In recommender systems, it is quite common to model the preferences of new users by conducting short interviews with them [1,5,7,10,14]. Blumenstock et al [4] estimated the interests of users from search logs.…”

Section: Related Work 21 User Modeling With Questionnairementioning

confidence: 99%

Tackling Cannibalization Problems for Online Advertisement

Ueoka¹,

Tsubouchi²,

Sato³

2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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Market cannibalization is inevitable when there are two or more competing marketing approaches to the same customer base. The cannibalization problem has been discussed in the context of search advertising of individual advertisers, whereas in this paper we discuss the problem that advertising platform companies face in dealing with multiple advertisers. In online advertising, they must properly serve ads with varying mass appeal to users with various interests. For them, it is important to maximize the value of the ads for advertisers and also for the platform. To do so, they deploy user models to serve ads. However, shortsighted models could lead to a decrease in overall performance in an attempt to improve certain ads' performance while slightly impairing the rest. We consider this phenomenon from the perspective of cannibalization and confirm the existence of a cannibalization problem in optimizing the delivery of ads in minor categories. To resolve this problem, we propose new methods, apply them to an ad delivery system, and conduct an A/B test. Our methods overcame the cannibalization problem and increased revenue by + 0.6% compared with the baseline method.

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Section: Related Work 21 User Modeling With Questionnairementioning

confidence: 99%

Tackling Cannibalization Problems for Online Advertisement

Ueoka¹,

Tsubouchi²,

Sato³

2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations

Parambath

Chawla

2020

Data Min Knowl Disc

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Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.

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