inzicht - Information Retrieval - # Personalized Short-Video Search

Leveraging User Behaviors to Enhance Short-Video Search Engagement through Personalization

Q: How can the personalized retrieval and ranking models be further improved to capture more nuanced user behaviors and preferences?

To enhance personalized retrieval and ranking models for short-video search, several strategies can be employed to capture more nuanced user behaviors and preferences: Incorporation of Multi-Modal Data: By integrating various data types such as audio, visual, and textual features from videos, models can better understand user preferences. For instance, analyzing the audio content of videos alongside user interactions can reveal preferences for specific genres or styles that are not evident from text alone. Contextual User Profiles: Developing dynamic user profiles that adapt in real-time based on user interactions can significantly improve personalization. This includes not only historical data but also contextual factors such as time of day, location, and current trends. For example, a user might prefer different content during leisure hours compared to work hours. Advanced Behavioral Modeling: Utilizing deep learning techniques such as recurrent neural networks (RNNs) or transformers can help in modeling sequential user behaviors more effectively. This allows the system to capture the temporal dynamics of user interactions, providing insights into how preferences evolve over time. Feedback Loop Mechanisms: Implementing mechanisms that allow for continuous learning from user feedback can enhance model performance. By analyzing user interactions post-search, such as clicks, watch time, and likes, the system can refine its understanding of user intent and preferences, leading to more accurate retrieval and ranking. Diversity and Serendipity: Introducing diversity in search results can help in exposing users to new content that aligns with their interests but may not be directly inferred from their past behaviors. Techniques such as multi-objective optimization can balance relevance with diversity, ensuring users receive a mix of familiar and novel content.

Q: What are the potential challenges and limitations of applying personalization techniques to short-video search, and how can they be addressed?

Applying personalization techniques to short-video search presents several challenges and limitations: Data Privacy Concerns: The collection and utilization of user data for personalization raise significant privacy issues. To address this, platforms should implement robust data protection measures, ensure transparency in data usage, and provide users with control over their data preferences. Sparsity of User Interaction Data: Many users may not have sufficient interaction history, leading to challenges in accurately modeling their preferences. To mitigate this, hybrid approaches that combine collaborative filtering with content-based methods can be employed, allowing the system to leverage both user data and video attributes. Ambiguity in User Intent: Short queries can often be ambiguous, making it difficult to ascertain user intent. Enhancing the system's ability to disambiguate queries through contextual analysis and leveraging user behavior patterns can improve the accuracy of search results. Algorithmic Bias: Personalization algorithms may inadvertently reinforce existing biases by primarily recommending content similar to what users have previously engaged with. To counteract this, it is essential to incorporate fairness and diversity metrics into the ranking algorithms, ensuring a balanced representation of content. Scalability Issues: As the volume of content and user interactions grows, maintaining the efficiency and speed of personalized retrieval and ranking systems can become challenging. Implementing scalable architectures, such as distributed computing and efficient indexing techniques, can help manage large datasets effectively.

Q: How can the insights from this work on short-video search personalization be extended to other domains, such as e-commerce or social media platforms?

The insights gained from personalizing short-video search can be effectively extended to other domains, including e-commerce and social media platforms, in the following ways: User Behavior Analysis: Similar to short-video platforms, e-commerce and social media can benefit from analyzing user interactions to build comprehensive user profiles. By understanding purchase history, browsing patterns, and engagement metrics, these platforms can tailor recommendations to individual preferences. Contextual Recommendations: Just as short-video search leverages contextual information to enhance personalization, e-commerce platforms can utilize contextual data such as seasonal trends, user location, and current promotions to provide timely and relevant product recommendations. Multi-Modal Content Integration: The integration of various content types (e.g., images, videos, and text) can enhance user engagement across platforms. For instance, e-commerce sites can use video content to showcase products, while social media can incorporate user-generated videos to drive engagement. Real-Time Feedback Mechanisms: Implementing real-time feedback loops, as demonstrated in short-video search, can help e-commerce and social media platforms adapt quickly to changing user preferences. This can involve analyzing user interactions immediately after recommendations to refine future suggestions. Diversity and Exploration: The importance of balancing relevance with diversity in search results can be applied across domains. E-commerce platforms can introduce serendipitous recommendations to encourage users to explore new products, while social media can promote diverse content to enhance user experience and engagement. By leveraging these insights, platforms in various domains can enhance user engagement, satisfaction, and retention through effective personalization strategies.

Belangrijkste concepten

Personalization can significantly improve user engagement in short-video search by leveraging user profiles, long-term interests, and real-time behaviors to retrieve and rank relevant content.

Samenvatting

This work introduces PR2, a comprehensive solution for personalizing short-video search. It consists of two key components:

Personalized Retrieval:

Query-Relevant Collaborative Filtering (QRCF) leverages user's past watching history to retrieve videos relevant to the current search query.
Personalized Dense Retrieval (PDR) encodes user profiles and behaviors into the retrieval model to generalize and retrieve personalized candidates.

Personalized Ranking:

The Query-dominant Interest Network (QIN) ranking model effectively captures both users' long-term preferences and real-time behaviors through a multi-task learning framework.

Online A/B testing on a major short-video platform demonstrates substantial improvements in user engagement metrics:

10.2% increase in CTR@10
20% surge in video watch time
1.6% uplift in search DAU

The authors believe the practical insights presented in this work are valuable for building and improving personalized search systems, especially for short-video platforms.

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Over 80% of search users are highly active, logging in for more than 20 days per month and watching over 200 videos each day.
More than 1/4 of queries are issued while users are browsing recommendation feeds, providing crucial context for understanding search intent.
Over 40% of queries contain less than 6 Chinese characters, conveying ambiguous search needs and intentions.

Citaten

"Personalized search tailor search results to individual needs by incorporating user information beyond the input query."
"The brevity of input queries underscores the need for search engines to leverage user context and historical behaviors to disambiguate search intentions."
"We believe the practical insights presented in this work are valuable especially for building and improving personalized search systems for the short video platforms."

Belangrijkste Inzichten Gedestilleerd Uit

Beyond Relevance: Improving User Engagement by Personalization for Short-Video Search

by Wentian Bao,... om arxiv.org 09-18-2024

https://arxiv.org/pdf/2409.11281.pdf

Beyond Relevance: Improving User Engagement by Personalization for Short-Video Search

Diepere vragen

How can the personalized retrieval and ranking models be further improved to capture more nuanced user behaviors and preferences?

To enhance personalized retrieval and ranking models for short-video search, several strategies can be employed to capture more nuanced user behaviors and preferences:

Incorporation of Multi-Modal Data: By integrating various data types such as audio, visual, and textual features from videos, models can better understand user preferences. For instance, analyzing the audio content of videos alongside user interactions can reveal preferences for specific genres or styles that are not evident from text alone.

Contextual User Profiles: Developing dynamic user profiles that adapt in real-time based on user interactions can significantly improve personalization. This includes not only historical data but also contextual factors such as time of day, location, and current trends. For example, a user might prefer different content during leisure hours compared to work hours.

Advanced Behavioral Modeling: Utilizing deep learning techniques such as recurrent neural networks (RNNs) or transformers can help in modeling sequential user behaviors more effectively. This allows the system to capture the temporal dynamics of user interactions, providing insights into how preferences evolve over time.

Feedback Loop Mechanisms: Implementing mechanisms that allow for continuous learning from user feedback can enhance model performance. By analyzing user interactions post-search, such as clicks, watch time, and likes, the system can refine its understanding of user intent and preferences, leading to more accurate retrieval and ranking.

Diversity and Serendipity: Introducing diversity in search results can help in exposing users to new content that aligns with their interests but may not be directly inferred from their past behaviors. Techniques such as multi-objective optimization can balance relevance with diversity, ensuring users receive a mix of familiar and novel content.

What are the potential challenges and limitations of applying personalization techniques to short-video search, and how can they be addressed?

Applying personalization techniques to short-video search presents several challenges and limitations:

Data Privacy Concerns: The collection and utilization of user data for personalization raise significant privacy issues. To address this, platforms should implement robust data protection measures, ensure transparency in data usage, and provide users with control over their data preferences.

Sparsity of User Interaction Data: Many users may not have sufficient interaction history, leading to challenges in accurately modeling their preferences. To mitigate this, hybrid approaches that combine collaborative filtering with content-based methods can be employed, allowing the system to leverage both user data and video attributes.

Ambiguity in User Intent: Short queries can often be ambiguous, making it difficult to ascertain user intent. Enhancing the system's ability to disambiguate queries through contextual analysis and leveraging user behavior patterns can improve the accuracy of search results.

Algorithmic Bias: Personalization algorithms may inadvertently reinforce existing biases by primarily recommending content similar to what users have previously engaged with. To counteract this, it is essential to incorporate fairness and diversity metrics into the ranking algorithms, ensuring a balanced representation of content.

Scalability Issues: As the volume of content and user interactions grows, maintaining the efficiency and speed of personalized retrieval and ranking systems can become challenging. Implementing scalable architectures, such as distributed computing and efficient indexing techniques, can help manage large datasets effectively.

How can the insights from this work on short-video search personalization be extended to other domains, such as e-commerce or social media platforms?

The insights gained from personalizing short-video search can be effectively extended to other domains, including e-commerce and social media platforms, in the following ways:

User Behavior Analysis: Similar to short-video platforms, e-commerce and social media can benefit from analyzing user interactions to build comprehensive user profiles. By understanding purchase history, browsing patterns, and engagement metrics, these platforms can tailor recommendations to individual preferences.

Contextual Recommendations: Just as short-video search leverages contextual information to enhance personalization, e-commerce platforms can utilize contextual data such as seasonal trends, user location, and current promotions to provide timely and relevant product recommendations.

Multi-Modal Content Integration: The integration of various content types (e.g., images, videos, and text) can enhance user engagement across platforms. For instance, e-commerce sites can use video content to showcase products, while social media can incorporate user-generated videos to drive engagement.

Real-Time Feedback Mechanisms: Implementing real-time feedback loops, as demonstrated in short-video search, can help e-commerce and social media platforms adapt quickly to changing user preferences. This can involve analyzing user interactions immediately after recommendations to refine future suggestions.

Diversity and Exploration: The importance of balancing relevance with diversity in search results can be applied across domains. E-commerce platforms can introduce serendipitous recommendations to encourage users to explore new products, while social media can promote diverse content to enhance user experience and engagement.

By leveraging these insights, platforms in various domains can enhance user engagement, satisfaction, and retention through effective personalization strategies.