HiPpo‑Video Benchmark for Personalized Highlights

• HiPpo‑Video Benchmark is a dataset and framework that enables personalized video summarization by simulating user watch histories and annotating multimodal video segments.
• It employs an LLM-driven simulator and advanced transformer-based methods with cross-attention to align segment features with user preferences.
• Experimental results show superior performance over query-based baselines, highlighting its potential for enhancing recommendation systems and interactive video browsing.

The HippoVlog Benchmark, often stylized as HiPpo‑Video, is a dataset and benchmarking framework designed to advance personalized video highlighting by leveraging simulated user watch histories and multi-modal video segment annotation. It was introduced to address limitations in existing video datasets—which typically lack personalization and rely solely on isolated videos or query-based labeling—and to enable rigorous evaluation of methods that condition video summaries and segment saliency predictions on individual user preferences as expressed in their watch history.

1. Dataset Construction and Structure

HiPpo‑Video comprises 2,040 watch history sequences, each containing 10 videos, totaling 20,400 distinct video instances sampled from 170 semantic categories. Each watch history sequence is paired with a saliency scoring annotation for the final (“target”) video, segmented via scene change detection. Segments are each assigned an integer saliency score (scale 1–10), indicating their predicted relevance to the simulated user’s historical preferences.

The core innovation in dataset construction is the use of a LLM-driven user simulator:

• Candidate Retrieval: At each session step, a pool of $l=8$ video candidates is assembled considering both the long-term preference embedding $p_{i-1}$ (representing accumulated history) and short-term signals extracted from the metadata of the three most recently watched videos.
• Video Engagement and Segment Representation: The simulator selects the most and least preferred candidate and “views” the chosen video. Each segment $s_j$ is annotated with a visual description (obtained via frame captioning using a pretrained CLIP image encoder) and its transcript.
• Preference Modification: The simulator refines user preference $p_i$ through a contrastive mechanism: after each engagement, a rationale is generated for both most and least wanted selections, further updating the user profile.

Initial user profiles sample from 170 topic-subtopic pairs and sentiment variables, ensuring diversity of simulated behaviors and interests.

2. Methodology: History-driven Highlighting with HiPHer

The HiPHer framework (“History-driven Preference-aware Video Highlighter”) is proposed for personalized segment-wise saliency prediction. The architecture operates as follows:

• Preference Embedding: Each watched video in the history is encoded by aggregating segment-level multimodal features (visual and transcript) into a video embedding $h^{(i)}$. The overall user preference representation is computed via mean pooling:

$$e_p = Agg_h(\{h^{(i)} : h^{(i)} = Agg_s(s_1^{(i)}, \ldots, s_n^{(i)})\}_{i=1}^m)$$

where $Agg_s$ is a segment pooling function and $Agg_h$ is mean pooling across videos.

• Segment Feature Extraction and Alignment: Each target video segment is mapped into a shared feature space by concatenating its CLIP-based visual and transcript features, processed through projection layers that include LayerNorm and dropout.
• Cross-attention: A cross-attention module uses the preference embedding $e_p$ as keys/values, conditioning each segment query on historical user interests.
• Transformer Encoder: The aggregated attention outputs are then fed to a transformer encoder, producing final segment-wise saliency scores.
• Loss Function: Segment saliency prediction is optimized using a margin-based contrastive loss:

$$\mathcal{L}_{saliency} = \sum_{(v^+, v^-)} \max(0, \gamma - (y^+ - y^-))$$

Here, $v^+$ and $y^+$ refer to relevant segments and scores, $v^-, y^-$ to irrelevant ones, and $\gamma$ is a set margin.

3. Experimental Results and Benchmarking

The HiPpo‑Video dataset and the HiPHer method are validated through extensive experiments:

• Training/Test Splits: 70/30 splits for training and evaluation; cross-dataset generalization is evaluated using QVHighlights for comparison.
• Baselines: HiPHer is compared against state-of-the-art approaches in highlight detection (SL-Module, Moment-DETR), moment retrieval (UMT, QD-DETR), and video summarization (UVCOM, TR-DETR).
• Performance Metrics: HiPHer exhibits superior results in terms of
  • RMSE for score prediction (RMSE = 0.301 on HiPpo‑Video)
  • Mean average precision (mAP = 0.766)
  • Hit@1 ranking metrics
  • Recall@1 for moment retrieval
  • F1 scores for summarization
• Ablation studies confirm that longer and richer watch histories markedly improve highlight detection compared to query-based methods.

4. Comparative Analysis and Benchmark Positioning

HiPpo‑Video is distinguished from existing video benchmarks in several key dimensions:

Benchmark	Personalization	Modalities	Dataset Size / Scope
HiPpo-Video	History-driven	Visual + Transcript	2,040 histories, 20,400 videos, 170 categories
QVHighlights	Query-based	Visual + Transcript	smaller, less diverse, no full history representation
TVSum/SumMe	No	Visual	generic summarization datasets, no preference modeling
YouTubeHighlights	No	Visual	query-based, not user-personalized

Unlike query-based or generic datasets, HiPpo‑Video’s “history-driven” approach (Editor's term) enables nuanced preference conditioning and segmentation, capturing both short and long-term interests. Its LLM-based user simulation offers scalable, privacy-preserving dataset expansion.

5. Applications and Research Implications

The HippoVlog Benchmark has tangible impact in several application spaces:

• Personalized Video Summarization: Enables streaming platforms to generate highly individualized highlights, facilitating efficient consumption of long-form media.
• Recommendation Systems: Enhances targeted recommendations by conditioning results on temporal preference evolution rather than isolated queries.
• Interactive Browsing: Supports dynamic video browsing interfaces that adapt segment selection to inferred user interests.
• Scalable Simulation for Training Data: The use of LLM simulators provides a renewable source of training data, circumventing privacy concerns and user data collection constraints.

This suggests growing utility for simulated user-based approaches in multimedia benchmarking and personalization research.

6. Limitations and Future Directions

Despite its advantages, several considerations remain:

• The reliance on simulated watch histories, while addressing privacy and scale, may introduce distributional gaps compared to real user data. A plausible implication is the need for future benchmarking rounds that blend simulated and real histories for increased ecological validity.
• Expanding modalities to incorporate user interaction signals (e.g., explicit feedback, skips) is another direction.
• HiPpo‑Video’s focus on highlight saliency may be extendable to tasks such as personalized moment retrieval, question answering, and narrative structure prediction.

Nuanced aggregation functions and advanced cross-modal attention mechanisms could further refine personalization capabilities, while alignment strategies for real-world deployment merit exploration.

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The HippoVlog Benchmark provides a state-of-the-art platform for personalized video highlighting, leveraging multimodal input representations, simulated user histories, and rigorous evaluation protocols to advance the paper and engineering of preference-conditioned video understanding.