insight - Open-app behavior modeling - # Open-App Motivation Prediction

Predicting Users' Motivation to Open Apps: Search or Recommendation

Q: How can the insights from open-app motivation prediction be leveraged to further improve the overall user experience in apps that integrate search and recommendation services?

The insights gained from open-app motivation prediction can be leveraged to enhance the overall user experience in apps that integrate search and recommendation services in several ways: Personalized Recommendations: By accurately predicting a user's open-app motivation, apps can provide more personalized recommendations tailored to the user's specific intent. This can lead to higher user engagement and satisfaction as users are more likely to find content that aligns with their preferences. Optimized User Interface: Understanding whether a user is opening the app to search for specific information or to explore recommended content can help in optimizing the user interface. For example, the app can prioritize search features for users who are more likely to search, and highlight recommended content for users who prefer exploration. Dynamic Content Delivery: Based on the predicted open-app motivation, apps can dynamically adjust the content delivery strategy. For instance, if a user is predicted to be in a search mode, the app can surface relevant search results prominently, whereas for a user in exploration mode, the app can showcase personalized recommendations. Improved User Engagement: By catering to the user's specific intent when opening the app, the overall user engagement can be enhanced. Users are more likely to spend more time on the app and interact with the content if it aligns with their motivations. Reduced Cognitive Load: Tailoring the app experience based on predicted open-app motivations can reduce cognitive load for users. By presenting relevant content upfront, users can quickly find what they are looking for, leading to a more seamless and enjoyable user experience.

Q: How might the proposed NHP-OAM model be adapted or extended to address open-app motivation prediction challenges in other domains beyond video and e-commerce platforms?

The NHP-OAM model can be adapted or extended to address open-app motivation prediction challenges in other domains beyond video and e-commerce platforms by: Domain-specific Feature Engineering: Modify the model to incorporate domain-specific features and behavioral patterns relevant to the new domain. For example, in a social media platform, factors like user interactions, content preferences, and engagement metrics could be crucial for predicting open-app motivations. Customized Intensity Functions: Develop customized intensity functions that capture the unique temporal dependencies and user behaviors specific to the new domain. This can involve designing novel architectures or incorporating domain-specific constraints. Data Preprocessing: Adjust the data preprocessing steps to align with the characteristics of the new domain. This may involve redefining session boundaries, incorporating additional contextual information, or handling different types of user interactions. Transfer Learning: Utilize transfer learning techniques to transfer knowledge from the video and e-commerce domains to the new domain. Pre-trained models can be fine-tuned on the new dataset to leverage the learnings from the original domain. Evaluation Metrics: Define domain-specific evaluation metrics to assess the performance of the model accurately in the new domain. These metrics should align with the objectives and user behaviors specific to the domain. By adapting and extending the NHP-OAM model in these ways, it can effectively address open-app motivation prediction challenges in diverse domains, providing valuable insights and enhancing user experiences beyond video and e-commerce platforms.

Core Concepts

Accurately predicting whether users open an app with the intent to search or explore recommended content can significantly enhance user engagement and downstream application performance.

Abstract

The paper proposes a novel Neural Hawkes Process-based Open-App Motivation prediction model (NHP-OAM) to address the challenges in accurately predicting users' open-app motivations.

Key highlights:

Open-app motivation prediction is a critical problem for apps that integrate search and recommendation (S&R) services, as it can help optimize user engagement and improve downstream tasks.
Challenges include time-user bias, the influence of multiple factors (search queries, clicked items, etc.), and the sequential and temporal dependencies in user behaviors.
NHP-OAM leverages the Neural Hawkes Process to capture the Periodicity and Repeat-Query features in open-app motivations, addressing the time-user bias challenge.
A hierarchical transformer model is used to encode the multiple factors influencing open-app motivation, and a novel intensity function is designed to capture the Relevance feature.
Temporal information and user-specific embeddings are integrated to predict the final open-app motivation.
Experiments on a new real-world Open-App Motivation Dataset (OAMD) and an extended public S&R dataset ZhihuRec demonstrate the superiority of NHP-OAM over baseline models.
Further downstream application experiments highlight the effectiveness of NHP-OAM in predicting users' open-app motivations.

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Stats

The ratio of search to recommendation in users' open-app motivations exhibits clear daily and weekly patterns.
The higher the user's activity level in open-app to search, the higher the repeat-query ratio.
There is a positive correlation between the ratio of searches to clicked recommended items in a user's past sessions and their motivation to search when opening the app next time.

Quotes

"Accurately predicting whether users open an app with the intent to search or explore recommended content can significantly enhance user engagement and improve downstream application performance."
"Open-app motivation exhibits Periodicity and Repeat-Query features, and is influenced by multiple factors including search queries, clicked items, and their temporal occurrences."

Key Insights Distilled From

To Search or to Recommend

by Zhongxiang S... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03267.pdf

Deeper Inquiries

How can the insights from open-app motivation prediction be leveraged to further improve the overall user experience in apps that integrate search and recommendation services?

The insights gained from open-app motivation prediction can be leveraged to enhance the overall user experience in apps that integrate search and recommendation services in several ways:

Personalized Recommendations: By accurately predicting a user's open-app motivation, apps can provide more personalized recommendations tailored to the user's specific intent. This can lead to higher user engagement and satisfaction as users are more likely to find content that aligns with their preferences.

Optimized User Interface: Understanding whether a user is opening the app to search for specific information or to explore recommended content can help in optimizing the user interface. For example, the app can prioritize search features for users who are more likely to search, and highlight recommended content for users who prefer exploration.

Dynamic Content Delivery: Based on the predicted open-app motivation, apps can dynamically adjust the content delivery strategy. For instance, if a user is predicted to be in a search mode, the app can surface relevant search results prominently, whereas for a user in exploration mode, the app can showcase personalized recommendations.

Improved User Engagement: By catering to the user's specific intent when opening the app, the overall user engagement can be enhanced. Users are more likely to spend more time on the app and interact with the content if it aligns with their motivations.

Reduced Cognitive Load: Tailoring the app experience based on predicted open-app motivations can reduce cognitive load for users. By presenting relevant content upfront, users can quickly find what they are looking for, leading to a more seamless and enjoyable user experience.

How might the proposed NHP-OAM model be adapted or extended to address open-app motivation prediction challenges in other domains beyond video and e-commerce platforms?

The NHP-OAM model can be adapted or extended to address open-app motivation prediction challenges in other domains beyond video and e-commerce platforms by:

Domain-specific Feature Engineering: Modify the model to incorporate domain-specific features and behavioral patterns relevant to the new domain. For example, in a social media platform, factors like user interactions, content preferences, and engagement metrics could be crucial for predicting open-app motivations.

Customized Intensity Functions: Develop customized intensity functions that capture the unique temporal dependencies and user behaviors specific to the new domain. This can involve designing novel architectures or incorporating domain-specific constraints.

Data Preprocessing: Adjust the data preprocessing steps to align with the characteristics of the new domain. This may involve redefining session boundaries, incorporating additional contextual information, or handling different types of user interactions.

Transfer Learning: Utilize transfer learning techniques to transfer knowledge from the video and e-commerce domains to the new domain. Pre-trained models can be fine-tuned on the new dataset to leverage the learnings from the original domain.

Evaluation Metrics: Define domain-specific evaluation metrics to assess the performance of the model accurately in the new domain. These metrics should align with the objectives and user behaviors specific to the domain.

By adapting and extending the NHP-OAM model in these ways, it can effectively address open-app motivation prediction challenges in diverse domains, providing valuable insights and enhancing user experiences beyond video and e-commerce platforms.