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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →A generative recommender needs interaction data linked to a usable item catalog. The exact fields depend on what it must do: ordered events and timestamps are important for next-item prediction, while item text, images, or other content belong only when the model uses them. Prepare the data by defining the task, standardizing events and IDs, preserving relevant time and exposure context, auditing coverage, preventing future-data leakage, and limiting personal data to what the purpose requires.
What data does a generative recommender need?
Start with the prediction job, not a checklist of fields. A model that ranks candidates, predicts a next item, estimates a rating, or supports conversational discovery can need different inputs. Generative recommenders may learn from interactions directly or use pretrained text and multimodal capabilities; the model and task determine which catalog content is useful. The 2026 Gen-RecSys survey describes this range of approaches and evaluation needs.
| Data category | What it can contain | When it matters |
|---|---|---|
| Interactions | User or session key, item key, event type, and—when available—event time and task-relevant context | The baseline for learning from behavior, whether the task uses ratings, clicks, views, purchases, or other feedback |
| Item catalog | Stable item IDs and information needed to identify or describe candidates, such as attributes or text | Needed to connect events to items; richer content is useful when the model consumes it or the task requires it |
| Sequence and context | Chronological event order, timestamps, and relevant context | Important for next-item and session recommendation, and for evaluating changes in interests over time |
| Exposure and collection records | How events were captured and what users had an opportunity to see | Needed to interpret observed behavior in light of how the data was collected |
| Optional content modalities | Text, images, video, or other task-relevant item content | Include only if the chosen model and use case actually rely on them |
The 2026 dataset survey identifies ratings and reviews as explicit feedback, and clicks, views, and purchases as common implicit signals. Keep their meanings distinct: a view is not a purchase, and a rating is not simply another click. Treating unlike events as interchangeable can give the model and evaluators a misleading account of user behavior.
Interactions are the starting point, not the whole dataset
For each event, preserve enough information to connect the behavior to a user or session and an item. Use a stable item identifier that joins reliably to the catalog. If the system is meant to personalize by user, a user key may be appropriate; if the task is limited to a session, a session key may be sufficient. Which identifier to retain should follow the task and privacy requirements, not a habit of collecting every available field.
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Catalog content is model-dependent
A catalog needs identifiers that remain consistent between interaction records and candidate items. Depending on the task, it may also include item attributes and descriptions. Generative-recommender literature covers image and video inputs as well as text, but that does not make every modality a requirement. Unused fields add preparation and governance work without necessarily helping the intended prediction.
Sequences need order; temporal evaluation needs time
For next-item or session recommendation, event order is part of the signal: the same set of events in a different order can represent a different sequence. Timestamps also matter when the question involves preference drift or short-term versus long-term interest. There is no universal history length established by the reviewed literature; a short recent sequence can suit some session tasks, while longer-term personalization may call for more history.
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Exposure records help explain observed behavior
An interaction records what happened, not necessarily what a person preferred among all possible items. A user cannot click an item they were never shown. The 2026 dataset survey recommends documenting how interactions were recorded and what exposure influenced behavior so that observed signals are interpreted in context rather than treated as direct, complete preference measurements.
How should you prepare the data?
- Define the target. State whether the system predicts ratings, ranks candidates, predicts the next item, supports conversational discovery, or processes item content. Specify the unit of prediction and the information available at prediction time. That decision determines which events, labels, histories, and modalities are relevant.
- Build a canonical event schema. Standardize user or session IDs, item IDs, event names, timestamps and time zones, and missing-value conventions. Define each event precisely and keep explicit feedback separate from implicit behavior. Consistent schema design makes records comparable and lets events join cleanly to the catalog.
- Check catalog joins and identifiers. Verify that interaction item IDs resolve to catalog records, that IDs are stable across data sources, and that duplicate or retired items are handled according to the task. Record how unmatched events and catalog changes are treated rather than silently discarding them.
- Preserve chronology and create leakage-safe targets. For sequence tasks, order events by time and ensure that training inputs contain only information available before the prediction target. Do not let a future interaction, or a feature derived from it, enter the model’s earlier input. When evaluating change over time, retain the time information needed to represent that change.
- Audit coverage and representativeness. Examine scale alongside sparsity, domain diversity, event mix, time coverage, missing context, and the representation of users and item categories. High sparsity can make user-item similarities harder to learn and can disadvantage cold-start users or long-tail items. Dataset choice can also change measured model performance, so a large dataset is not automatically a suitable one.
- Document collection and transformations. Record instrumentation and collection method, exposure context, the time range, filtering and deduplication rules, exclusions, and any other transformations that affect what an event means. The 2026 survey also calls for clearer documentation of underrepresented groups and categories.
- Choose evaluation data and measures for the intended use. Select splits that reflect the question the system must answer; a static split cannot establish how a system handles temporal change if it contains no relevant temporal information. Consider ranking quality and efficiency. For conversational or generative systems, evaluation may also need to examine dialogue quality, engagement, longitudinal effects, and possible social harm, rather than accuracy alone. The Gen-RecSys survey treats evaluation as broader than a single accuracy measure.
- Apply privacy limits from the design stage. Decide which identifiers and personal attributes are necessary for the stated purpose, who can access them, and how long they need to be retained. Where the GDPR applies, Article 5(1)(c) requires personal data to be “adequate, relevant and limited to what is necessary” for its purposes; Article 25 requires appropriate data-protection-by-design and default measures. The regulation does not, by itself, determine the lawful basis or establish that a particular deployment complies.
What does a useful prepared dataset look like?
Use a documented schema that makes event meaning, sequence, and catalog linkage explicit. The following is an illustrative record layout, not a mandatory standard:
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- Interaction record: user or session key; item key; event type; event time with a defined time zone; and only the context needed for the target task.
- Item record: stable item key; relevant catalog attributes or description; and any additional modality the model actually consumes.
- Collection documentation: event definitions, instrumentation and collection method, exposure context where known, time range, filtering and deduplication decisions, exclusions, and known representation gaps.
- Evaluation definition: prediction target, information available at prediction time, split method, and measures matched to the intended setting.
These are practical implementation choices derived from the data needs described in the dataset survey and Gen-RecSys review; they are not a single schema prescribed by those sources. The AAAI 2026 paper Data-Centric Sequential Recommendation with Relation-Augmented Generation describes one research approach that standardizes sequential data, extracts LLM-derived semantic representations, and uses a multi-relation graph to generate augmented datasets. That is an example of a method, not a universal preprocessing step or evidence that generated augmentation will improve another dataset.
How should you compare datasets or approaches?
Compare candidates against the actual task and deployment conditions rather than choosing by record count alone. The following questions expose common mismatches:
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- Domain and catalog: Do the items and attributes resemble what the system will recommend?
- Feedback: Are the available signals explicit ratings, implicit behavior, or both, and are their meanings documented?
- Sequence and time: Are event order and timestamps available if the task or evaluation is temporal?
- Scale and sparsity: How concentrated are interactions, and are cold-start users or long-tail items represented?
- Context and exposure: Is it possible to understand how users encountered the items and how events were recorded?
- Modalities: Does the dataset provide the text or other content the selected model actually uses?
- Evaluation fit: Does the protocol resemble the prediction setting and include the relevant quality, efficiency, or conversational outcomes?
- Access and governance: Do access restrictions, retention needs, and applicable privacy obligations fit the intended use?
Also distinguish a model trained directly on interaction data from one that relies on pretrained text or multimodal capabilities. The latter still needs suitable task inputs and an evaluation protocol that tests the intended behavior; pretrained capabilities do not remove the need to check data fit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is there a minimum dataset size?
The reviewed sources do not establish a universal minimum number of records, interactions, or fields for every generative recommender. Requirements vary with the task, domain, model, catalog, and evaluation method. The Netflix Prize dataset’s more than 100 million movie ratings is historical scale context cited in the 2026 dataset survey, not a recommended threshold. Assess whether the data is sufficient for the intended setting by examining coverage, sparsity, sequence and time information, representation, and evaluation fit—not by treating one headline count as a rule.
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