Analyzing the Browsing Basket: A Latent Interests-Based Segmentation Tool

“…Therefore, in traditional marketing methods, audiences are divided into specific user groups through segmentation, and advertisements are presented to potential client groups as the "target," who are relevant to and interested in the advertisement. One of the most used segmentation methods is interest-based segmentation [22], which groups users with similar interests. For example, if a user is interested in ingredient analysis for cosmetics or fairtrade products, then we can predict that he/she and other users in a similar group (i.e., segment) may also be interested in organic products.…”

Section: B Segmentation For Ctr Predictionmentioning

confidence: 99%

Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online Advertising

2021

View full text Add to dashboard Cite

Online advertising is becoming an important direction in the advertising industry with its strengths in diverse users, strong interactions, real-time feedback, and expandability. Online advertisement (Ads) can show great marketing ability by processing data from multiple channels to convey information, understanding what users want, and approaching them easily. Moreover, predicting the click-through rate (CTR) can increase advertisement revenue and user satisfaction. However, advertising data contains many features, and the amount is growing rapidly. This can be alleviated through the segmentation of users with similar interests. We assumed that the change of interest of a user could be predicted by other users' change of interest. More specifically, similar users will change their interest in a similar direction. On the basis of this idea, we proposed a novel model, the Deep User Segment Interest Network, to improve CTR prediction. We suggested three novel layers for improving performance: i) an individual interest extractor, ii) a segment interest extractor, and iii) a segment interest activation. These layers captures the latent interest of each user and creates the expressive interest representation of the segment by aggregating each user's interest. We conducted experiments using TaoBao data, which are a kind of real commercial data from an advertising platform, to confirm the CTR prediction improvement by reflecting the segment interest. The proposed algorithm obtained an AUC gain of 0.0029 with a behavior sequence length of 100. This performance exhibited the greatest improvement over other baselines, indicating the proposed method's potential contribution to business improvement.

show abstract

“…on an apparel retailer's website or dependences between different genres on a book retailer website). Schröder et al (2019) show that many publications analyze Internet browsing behavior on the website of one firm or across types of websites (types may, e.g., consist of all book, travel or music sites), but very few investigate browsing behavior across websites of different individual firms. Applying this comprehensive approach, researchers and decision makers can get a better understanding of the journey of each customer.…”

Section: Introductionmentioning

confidence: 99%

“…Applying this comprehensive approach, researchers and decision makers can get a better understanding of the journey of each customer. To fill this research gap, Schröder et al (2019) determine topics underlying online users' browsing behavior by means of a popular topic model, latent Dirichlet allocation (LDA). They conceive the websites that a user visits during a calendar week as browsing basket in analogy to shopping baskets that are well known in retailing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing browsing across websites by machine learning methods

Falke

Hruschka

2021

J Bus Econ

Self Cite

View full text Add to dashboard Cite

The increasing importance of online distribution channels is paralleled by a rising interest in gaining insights into the customer journey and browsing behavior. We evaluate several machine learning methods (latent Dirichlet allocation, correlated topic model, structural topic model, replicated softmax model) with respect to their ability to reproduce the browsing behavior of households across websites. In addition, we compare these machine learning methods to a related classical technique, singular value decomposition. In our study, the replicated softmax model outperforms latent Dirichlet allocation, but the correlated topic model attains the overall best performance. Compared to singular value decomposition both the correlated topic model and the replicated softmax model lead to a more efficient compression of web browsing data. On the other hand, singular value decomposition surpasses latent Dirichlet allocation. We interpret results of the correlated topic model and the replicated softmax model by determining combinations of topics or hidden variables that are heterogeneous with respect to visited websites. We show that decision makers should not rely on bivariate measures of site visits, as these do not agree with measures of interdependences between sites that can be inferred from the correlated topic model or the replicated softmax model. We investigate how well topics or hidden variables measured by these methods predict yearly household expenditures. The correlated topic model leads to the best predictive performance, followed by the replicated softmax model. We also discuss how the replicated softmax model can be used to support online marketing decisions of websites.

show abstract

Analyzing the Browsing Basket: A Latent Interests-Based Segmentation Tool

Cited by 17 publications

References 41 publications

Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data

Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data

Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online Advertising

Analyzing browsing across websites by machine learning methods

Contact Info

Product

Resources

About