HIPPO-Video: Personalized Video Highlighting

• HIPPO-Video is a dataset and modeling framework for personalized video highlighting that uses simulated watch histories and segment-wise saliency scores.
• The system employs an LLM-based user simulator to generate session-scale preferences, updating user states across multiple video interactions.
• The approach aligns segment saliency with individual viewing behaviors, leading to improved highlight detection over generic and query-based methods.

Searching arXiv for HIPPO-Video and closely related video highlighting work to ground the article in current literature. arXiv search query: "HIPPO-Video personalized video highlighting HiPHer" HIPPO-Video is a dataset and modeling framework for personalized video highlighting in which each instance consists of a simulated user watch history and personalized segment-wise saliency scores for a target video. It was introduced together with HiPHer, a history-driven preference-aware video highlighter, to address the mismatch between real user preferences and the generic or query-based supervision used in prior highlight and summarization benchmarks. The central premise is that user preference is better represented by a session-scale watch history than by an isolated video or a single short query, and that such histories can be synthesized at scale by an LLM-based user simulator without relying on private real-world logs (Lee et al., 22 Jul 2025).

1. Problem setting and motivation

HIPPO-Video targets Personalized Video Highlighting (PV), where the objective is not to identify a single universally salient summary, but to score segments according to an individual viewer’s inferred preferences. In this formulation, highlight quality is explicitly user-conditioned: the same video may admit different high-value segments for different watch histories.

The work is motivated by two shortcomings of existing resources. First, many video summarization, highlight detection, and moment retrieval datasets are generic, assuming one set of important moments for all viewers. Second, query-conditioned datasets reduce user intent to a short phrase or sentence. The paper argues that this is inadequate because real preferences are multi-dimensional, may depend on topic, style, sentiment, and pacing, and can evolve during a session rather than remain fixed (Lee et al., 22 Jul 2025).

A further obstacle is data collection. Real watch histories are both private and difficult to annotate at segment level. Manual acquisition of per-user, per-segment saliency labels is correspondingly expensive. HIPPO-Video addresses this by using an LLM-based simulator that generates watch histories, preference updates, and final segment scores in a closed loop grounded in real video metadata and content descriptions. This suggests a shift from static personalization signals to session-scale behavioral simulation as the primary source of supervision.

2. Dataset structure and statistical profile

The dataset is organized around 10-video watch histories. For each history, the 10-th video serves as the target video, and its segments are assigned personalized saliency scores in $[1,10]$. The resource contains 2,040 $(\text{watch history}, \text{saliency score})$ pairs, covering 20,400 videos across 170 semantic categories (Lee et al., 22 Jul 2025).

Attribute	Value
Watch histories	2,040
Total videos	20,400
Semantic categories	170
Videos per history	10
Average video length	13.9 minutes
Length range	30 seconds to 119 minutes
Average target segments	56.91

Within a history, each video $H_i$ is represented as scene-based segments,

$$H_i = \{ s_1^{(i)}, s_2^{(i)}, \dots, s_{n_i}^{(i)} \},$$

and each segment is described as

$s_k^{(i)} = (v_k^{(i)}, t_k^{(i)}),$

where $v_k^{(i)}$ is a visual description and $t_k^{(i)}$ is transcript text. For modeling, a representative frame and transcript are later embedded with CLIP and concatenated.

The dataset supports Video Summarization (VS), Moment Retrieval (MR), Highlight Detection (HD), and Personalized Video Highlighting (PV). The paper emphasizes that it is the only dataset in its comparison that combines multiple videos per user session with personalized saliency. The temporal profile is contemporary: videos span 2008–2024, and 57.16% are from after 2023. The reported exploration ratio typically lies between 0.2 and 0.6, indicating that some simulated users remain focused while others drift substantially across topics. Saliency distributions are moderately dispersed, with per-video mean saliency typically between 4 and 6 and standard deviation between 1.5 and 2, while larger standard deviation values indicate more strongly differentiated segment importance (Lee et al., 22 Jul 2025).

3. LLM-based user simulator and annotation pipeline

The data generation pipeline begins from 170 topic/subtopic combinations, obtained as 17 main topics × 10 subtopics each, and combines these with one of four intent variables: amusing, emotional, informative, or recent news. This yields 2,040 unique initial preference seeds. An example seed is “I like recent news on Job, especially about Lawyer” (Lee et al., 22 Jul 2025).

Simulation proceeds for $m=10$ steps. At step $i$, the simulator has access to previously watched videos $\mathcal{H}_{i-1}$, a current long-term preference state $(\text{watch history}, \text{saliency score})$0, and a candidate set

$(\text{watch history}, \text{saliency score})$1

Each iteration contains three phases: Video Candidate Retrieval, Video Engagement, and Preference Update.

In candidate retrieval, the simulator either continues with related videos or issues a new search query. In video engagement, it selects the most wanted video and the least wanted video from the candidate pool. The selected video is segmented with PySceneDetect; each segment receives a visual description from a visual-instruction-tuned model and a transcript from ASR. The simulator then generates a two-sentence summary and a two-sentence personal opinion referencing one or two preferences. These reviews become inputs to the next preference update.

Preference update is explicitly rule-based at the prompt level: addition, refinement, and removal. The long-term preference $(\text{watch history}, \text{saliency score})$2 is updated from $(\text{watch history}, \text{saliency score})$3, the accumulated reviews, and the rationales for both most and least wanted videos. The resulting preference statements become more specific over time, moving from broad topical affinity to fine-grained likes and dislikes about narrative structure, technological themes, pacing, and presentation style. A plausible implication is that the simulator is designed not merely to imitate browsing order but to emulate preference formation across a session.

After the tenth step, the final video is treated as the target $(\text{watch history}, \text{saliency score})$4. For each target segment, the simulator receives the final long-term preference $(\text{watch history}, \text{saliency score})$5, the video-specific reviews, and the text representations of segment content, and assigns a saliency score

$(\text{watch history}, \text{saliency score})$6

These scores are intended to capture preference-conditioned appeal, engagement, pacing, and impact (Lee et al., 22 Jul 2025).

4. Formal task definition and HiPHer

The personalized highlighting task is defined over a watch history

$(\text{watch history}, \text{saliency score})$7

and a target video

$(\text{watch history}, \text{saliency score})$8

The goal is to predict

$(\text{watch history}, \text{saliency score})$9

where each $H_i$0 is a preference-conditioned segment saliency score.

HiPHer instantiates this with two modules: a Preference Modeling Module and a Scoring Module. For each target segment, a representative frame $H_i$1 and transcript $H_i$2 are encoded with CLIP:

$H_i$3

and concatenated as

$H_i$4

For history modeling, segment features within each watched video are mean-pooled into a per-video embedding

$H_i$5

and the video embeddings are then mean-pooled into a global preference embedding

$H_i$6

With mean pooling in both stages, the effective form is

$H_i$7

The scoring module projects segment features and $H_i$8 through LayerNorm and Dropout stacks, performs cross-attention with segment embeddings as queries and $H_i$9 as key and value, and then refines the resulting sequence with a Transformer encoder. A final linear layer or small MLP outputs segment scores. The conditioning mechanism is therefore global-history-to-local-segment rather than query-to-video alignment.

Training uses a margin ranking objective over segment pairs $$H_i = \{ s_1^{(i)}, s_2^{(i)}, \dots, s_{n_i}^{(i)} \},$$0 such that the ground-truth score of $$H_i = \{ s_1^{(i)}, s_2^{(i)}, \dots, s_{n_i}^{(i)} \},$$1 exceeds that of $$H_i = \{ s_1^{(i)}, s_2^{(i)}, \dots, s_{n_i}^{(i)} \},$$2:

$$H_i = \{ s_1^{(i)}, s_2^{(i)}, \dots, s_{n_i}^{(i)} \},$$3

The paper reports that smaller margins, such as 0.1–0.2, work better than larger margins. This suggests that the model benefits from preserving fine-grained saliency ordering rather than enforcing overly coarse separation (Lee et al., 22 Jul 2025).

5. Validation and empirical results

The paper evaluates both the realism of the synthetic data and the effectiveness of HiPHer. For simulator validation, human annotators judged 97.56% of simulator-generated queries reasonable, with Fleiss’ $$H_i = \{ s_1^{(i)}, s_2^{(i)}, \dots, s_{n_i}^{(i)} \},$$4. For video selection, the simulator’s chosen most preferred video matched the majority human choice in approximately 71–68% of cases, depending on the reporting variant. For saliency validation, roughly 98% of cases were majority-labeled “Agree,” indicating that the highest-saliency clips generally aligned with the described preference profile. When distinguishing real from simulated histories, GPT-4 achieved 40% accuracy, below random guessing, and Fast-DetectGPT obtained Hit@1 = 0.35, which the paper interprets as substantial confusion between real and simulated histories (Lee et al., 22 Jul 2025).

On the main HiPpo-Video benchmark, HiPHer outperformed all compared highlight detection and moment retrieval baselines. Its reported metrics were RMSE 0.301, mAP 0.766, Hit@1@7 0.507, Hit@1@9 0.166, R@[email protected] 0.452, and R@[email protected] 0.245. The strongest compared baselines included Moment-DETR, UMT, QD-DETR, UVCOM, and TR-DETR, but none matched HiPHer across all reported metrics. Generic highlight detection, exemplified by SL-Module, performed worst, which is consistent with the paper’s claim that user-conditioned saliency cannot be recovered reliably from generic importance alone.

The model also transferred to a real-user benchmark, HiPpo-Video-H, built from 40 real histories. There, HiPHer achieved RMSE 0.427, Hit@1@7 0.486, Hit@1@9 0.400, and [email protected] 0.624, again exceeding the compared baselines. This is notable because the training signal came from synthetic histories, yet the model retained an advantage on real user data.

Ablations reinforce the central design claims. Increasing history length produced monotonic improvement, indicating that longer watch histories expose more stable preference structure. In summarization, the conditioning signal mattered strongly: CLIP-It with no query achieved F1@5 0.564 and F1@7 0.211; sentence queries improved this to 0.658 and 0.234; HiPHer with full history reached 0.726 and 0.486. Modality ablation showed that transcript features were more informative than visuals alone, but the fusion was strongest: HiPHer-V obtained mAP 0.67, Hit@1@7 0.12, R@[email protected] 0.32; HiPHer-T improved to 0.74, 0.15, 0.39; full HiPHer reached 0.77, 0.17, 0.45 (Lee et al., 22 Jul 2025).

6. Positioning, limitations, and implications

HIPPO-Video is positioned at the intersection of video summarization, personalized recommendation, and LLM-based user simulation. Relative to generic summarization datasets such as TVSum and SumMe, and query-focused resources such as QVHighlights and Mr.HiSum, its defining change is that personalization is encoded as a history-conditioned behavioral trace rather than a static prompt. Relative to recommender systems, it shifts the prediction target from item-level ranking to segment-wise saliency inside a target video. Relative to prior LLM simulation work, it extends behavior synthesis to query generation, video choice, longitudinal preference revision, and final segment scoring within a single pipeline (Lee et al., 22 Jul 2025).

The paper also identifies several limitations. The dataset remains synthetic, even if validated against human judgment and real histories. Domain coverage is broad but not exhaustive, with 170 topic/subtopic seeds rather than unrestricted personalization. HiPHer’s preference modeling is deliberately simple, relying on mean pooling rather than temporal sequence models, recency weighting, or more structured user-state inference. Additional user signals such as clicks, dwell time, skip behavior, and partial watching are absent. The authors also note bias and fairness concerns: because the simulator is LLM-based, generated preferences may reflect cultural or linguistic biases in the underlying model.

These constraints shape the proposed future directions. The paper suggests richer preference models, privacy-preserving incorporation of real traces, broader behavioral signals, and extensions of the same simulation paradigm to recommendation, conversational systems, and other personalization tasks. A plausible implication is that HIPPO-Video functions less as a terminal benchmark than as an initial template for constructing history-conditioned multimodal datasets at scale.