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Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content

T0 review · 2 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that the standard explicit-implicit feedback split misses a third, intentional kind of feedback that users deliberately perform to shape recommendation feeds.

desk verdict Introduces a genuinely useful third feedback category, but the self-report basis and a swapped-percentage error mean it needs a careful revision, not a desk reject. read the letter →

arxiv 2502.09869 v1 pith:CXMPEAW5 submitted 2025-02-14 cs.HC

Wenqi Li , Jui-Ching Kuo , Manyu Sheng , Pengyi Zhang , Qunfang Wu This is my paper · ORCID
classification cs.HC
keywords personalizedrecommendationalgorithmsexplicitfeedbackimplicitintentionaluserpurposesemi-structuredinterviewXiaohongshuDouyin
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that the standard split of user feedback into explicit and implicit categories misses a central part of how people actually steer recommendation feeds. Based on semi-structured interviews with 34 active users of platforms like Xiaohongshu and Douyin, the authors claim that users often consciously perform behaviors traditionally counted as implicit feedback—swiping past posts, clicking into content, searching topics, even pausing on a video—with the explicit intention of teaching the algorithm. They propose a three-way taxonomy: explicit feedback, intentional implicit feedback, and unintentional implicit feedback. They also report that feedback choices track user purposes: explicit feedback is used mainly for feed customization, unintentional implicit feedback for content consumption, and intentional implicit feedback for increasing diversity and relevance. If right, the finding gives designers a way to recognize and respond to intentional signals that current systems tend to misread or ignore.

What carries the argument

The central object is the three-way feedback taxonomy built from interview coding: explicit feedback (direct preference input), intentional implicit feedback (conscious behavior performed to influence recommendations), and unintentional implicit feedback (natural interaction without feedback intention). The machinery that carries the argument is the researchers' coding procedure: inductive coding of transcripts, deductive mapping onto existing explicit and implicit categories, then splitting implicit feedback by whether participants reported intention, and finally code co-occurrence analysis linking feedback types to four user purposes (content consumption, directed information seeking, content creation and promotion, and feed customization). Each behavior is also characterized by platform feature, polarity (positive or negative), and minimum scope (segment, object, or class), borrowed from prior frameworks, which is how the study shows intentional implicit feedback can carry negative polarity that standard implicit-feedback models assume away.

What would settle it

Give a panel of users a recommendation feed instrumented with logging and ask them immediately after each swipe, dwell, or search whether they intended to influence the feed; if stated intention does not systematically correspond to the logged action, or if the platform's recommendation changes do not track intentional signals better than unintentional ones, the three-way split would lose its predictive value.

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Extended reading notes

Core claim

The authors claim that the explicit-implicit feedback dichotomy, defined from the system's perspective, cannot fully capture user agency. Users knowingly employ behaviors the platform treats as implicit feedback—ignoring or fast-swiping posts, searching new topics, dwelling on content, even talking aloud for the platform to 'overhear'—to shape future recommendations. The study introduces intentional implicit feedback: behavior performed consciously with the expectation that the platform will interpret it as a preference signal, but without the direct input of explicit feedback. Across 34 interviews the authors found 6 explicit, 9 intentional implicit, and 13 unintentional implicit feedback behaviors, and a systematic alignment between feedback type and purpose: intentional implicit feedback dominates when users want more diverse or more relevant content, while explicit feedback dominates for removing inappropriate content. The conclusion is that the intention dimension belongs inside the feedback taxonomy, and that recognizing intentional implicit feedback would give users a greater sense of control and improve signal accuracy.

Load-bearing premise

The taxonomy rests on participants' own retrospective accounts that they performed those behaviors on purpose to influence the algorithm, with no platform log, screen recording, or moment-of-action check confirming the behavior or the intention, a point the authors acknowledge in their limitations.

Editorial extensions

If this is right

  • Platforms should treat swiping past, ignoring, searching, and dwelling as potentially intentional signals of preference, not just passive behavioral data.
  • Implicit feedback is not inherently positive; intentional negative signals such as fast-skipping a previously liked category or refreshing the feed should be captured and validated.
  • Feedback mechanisms should be surfaced according to purpose: explicit controls for removing unwanted content, subtle intentional signals for diversity and relevance tuning.
  • Designers can add lightweight confirmations (for example, 'see less of this?') and progress indicators so users know intentional implicit feedback was registered.
  • Recognizing intentional implicit feedback could reduce the cognitive cost of explicit feedback while preserving user agency.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next step the authors do not take: test whether classifying behavior by inferred intention improves recommendation quality metrics such as dwell time, satisfaction, or perceived control compared with behavior-only models; the taxonomy predicts it should.
  • The intention dimension connects to strategic classification: if users deliberately shape signals, platforms that ignore intention risk feedback loops where users escalate their strategies, as in the reported 'information cocoon' and platform switching.
  • Because the evidence is retrospective self-report, the taxonomy could be validated with in-the-moment experience sampling or platform telemetry; without that, it remains a user-perception model rather than a behavioral law.
  • The participant who talked aloud to be 'overheard' suggests that perceived surveillance itself becomes a feedback channel; a platform that legitimizes voice input could convert an opaque privacy violation into a transparent control.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper reports semi-structured interviews (N=34) with users of Chinese personalized recommendation platforms (e.g., Xiaohongshu, Douyin) to investigate how users provide feedback to shape their recommendation feeds. The authors propose a tripartite taxonomy of feedback: explicit, intentional implicit, and unintentional implicit, arguing that the traditional explicit/implicit dichotomy overlooks cases where users deliberately perform behaviors such as swiping past, ignoring, clicking, dwelling, or searching in order to influence future recommendations. They map these feedback types onto user purposes (content consumption, directed information seeking, content creation/promotion, and feed customization) and present co-occurrence frequencies and extensive illustrative quotes. The paper's contributions are the new concept of intentional implicit feedback, an empirical behavior-purpose mapping, and design implications for supporting purpose-oriented feedback on recommendation platforms.

Significance. If the taxonomy holds, it adds a user-intention dimension to the well-established explicit/implicit feedback distinction and connects it to prior work on folk theories and strategic interaction with algorithms (e.g., [10,43,50]). The study is empirically grounded: 34 interviews, systematic codebook thematic analysis (Section 3.3), detailed behavior tables (Tables 1 and 4), and many direct quotes that let the reader evaluate the interpretation. The authors also explicitly acknowledge in Section 5.4 that reported behaviors are not ground truth, which is a useful caveat. However, the core conceptual contribution turns on the reliability of retrospective self-reports of intention, which is not independently verified.

major comments (2)
  1. [Sections 3.2-3.3, central claim] Section 3.2 (interview procedure) and Section 3.3 (coding of intention): the paper's central distinction between intentional and unintentional implicit feedback rests entirely on participants' retrospective self-reports of their in-the-moment intentions. The protocol explicitly asks about 'strategies for managing content exposure and content preferences and avoiding undesirable content,' which primes participants to narrate ordinary interactions as strategic. Section 5.4 notes that reported behaviors are 'neither comprehensive nor reflective of the ground truth,' but this caveat concerns the completeness of the behavior list, not the possibility that retrospective rationalization creates the intentional/unintentional split. Because this is a load-bearing issue, the paper should either provide additional verification (e.g., diary or log-based confirmation, analysis of unprompted versus prompted mentions of intention, or a follow-up study) or reframe the central claim as being about users' perceptions and folk theories of their own feedback behavior rather than about the actual presence of intention at the moment of action. As written, the evidence establishes that users describe these behaviors as intentional, not that they were intentionally performed.
  2. [Section 3.3 (data analysis)] Section 3.3 (data analysis): the coding of intention is a researcher judgment, and no inter-rater reliability statistic (e.g., Cohen's kappa) or equivalent agreement audit is reported. The same observable behavior appears in both intentional and unintentional lists (e.g., 'Initiate a new search' in Table 1 vs. 'Search for information' in Table 4; 'Like a post' in both tables), so the boundary between categories is not self-evident. Without a reliability check, the reader cannot distinguish a stable emergent category from coder interpretation. I recommend adding a quantitative agreement measure or a detailed disagreement audit for the intentional/unintentional coding.
minor comments (4)
  1. [Section 5.2] Section 5.2: the percentages from Table 2 are swapped. The text attributes 75.9% to explicit feedback and 88.6% to intentional implicit feedback, but Table 2 shows 88.6% (31/35) for explicit feedback and 75.9% (44/58) for intentional implicit feedback in the feed customization row.
  2. [Table 1 / Section 5.1] Table 1 lists 20 participants for 'Ignore or swipe past a post,' while Section 5.1 states n=21 for this behavior; please reconcile the discrepancy.
  3. [Section 3.3] Section 3.3 uses 'potential correlation patterns' to describe what is actually a code co-occurrence analysis; please replace 'correlation' with 'co-occurrence' to avoid implying a statistical association.
  4. [Table 5] Table 5: P26's platform entry reads 'Douyin, Xiaohongshu,' with a trailing comma; this is a minor formatting error.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the intentional-implicit taxonomy is an inductively derived qualitative finding, not a fitted prediction or a self-citation chain.

full rationale

This paper is a semi-structured interview study with no formal derivation, fitted parameters, or predictive claims, so the standard circularity failure modes do not apply. The central concept, intentional implicit feedback, was developed inductively: the authors first observed in the transcripts that some behaviors previously classed as implicit were described by participants as deliberate attempts to shape the feed, and only then split the implicit category (Section 3.3: 'we found that within implicit feedback behaviors, users consciously and proactively shape the recommendation feeds, which contradicts the original definition of implicit feedback. Therefore, we divided implicit feedback into intentional implicit feedback and unintentional implicit feedback'). The finding is therefore not equivalent to its input by construction, since participants could have reported no such intent. The study contains no load-bearing self-citations: the reference list does not depend on the authors' own prior results to justify the taxonomy. The authors explicitly disclaim ground truth for the reported behaviors in Section 5.4 ('the feedback behaviors reported by users were neither comprehensive nor reflective of the ground truth'), which is a measurement-validity caveat, not a circularity. The closest semantic overlap is that 'unintentional implicit feedback' (behaviors 'without any deliberate intention to influence recommendation content') and 'content consumption' ('browsing the Explore page in Xiaohongshu undirectedly') share the property of non-strategic use, so the reported association between them is partly a consequence of the coding definitions; but the coding also allowed intentional implicit feedback during content consumption (9 instances in Table 2), so the co-occurrence result is not forced. The Section 5.2 text swaps the 75.9% and 88.6% percentages relative to Table 2; this is an internal consistency error, not a circularity. Overall, the derivation chain is self-contained and descriptively grounded in the interview data.

Assumptions & free parameters 0 free parameters · 2 assumptions · 1 invented entities

No numerical free parameters appear in this qualitative study. The main unstated assumptions are that participants' retrospective self-reports accurately capture their past behaviors and intentions, and that the platform features listed in the tables operate as participants believed. The introduced concept, intentional implicit feedback, is a coding construct with no independent behavioral or log-based validation, so its external validity rests on the interview sample.

assumptions (2)
  • domain assumption Interview self-reports are a valid window into users' past behaviors and intentions.
    The taxonomy of intentional versus unintentional feedback rests entirely on participants' retrospective statements in semi-structured interviews. No logs or direct observation are used to corroborate the reported actions.
  • domain assumption The platform features and their feedback affordances described in Table 1 and Appendix B are real and function as participants assumed.
    The authors map behaviors to platform features such as Not interested, search, and like, but do not verify against platform documentation or controlled experiments that these features influence recommendation feeds as users believe.
invented entities (1)
  • Intentional implicit feedback (as a feedback category)
    purpose: Captures user behaviors consciously performed with the expectation that the platform will interpret them as preference signals, splitting the existing implicit feedback category.
    The category is grounded only in participants' self-reported intentions in this study. No independent behavioral measure or external validation is provided, so its definition and applicability rest on the interview data alone.

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Cite this review

Pith. "Pith review of Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content." pith.science (2026). https://pith.science/paper/CXMPEAW5

@misc{pith2026250209869,
  author       = {Pith},
  title        = {Pith review of: Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CXMPEAW5}},
  note         = {Machine review of arXiv:2502.09869}
}
read the original abstract

As personalized recommendation algorithms become integral to social media platforms, users are increasingly aware of their ability to influence recommendation content. However, limited research has explored how users provide feedback through their behaviors and platform mechanisms to shape the recommendation content. We conducted semi-structured interviews with 34 active users of algorithmic-driven social media platforms (e.g., Xiaohongshu, Douyin). In addition to explicit and implicit feedback, this study introduced intentional implicit feedback, highlighting the actions users intentionally took to refine recommendation content through perceived feedback mechanisms. Additionally, choices of feedback behaviors were found to align with specific purposes. Explicit feedback was primarily used for feed customization, while unintentional implicit feedback was more linked to content consumption. Intentional implicit feedback was employed for multiple purposes, particularly in increasing content diversity and improving recommendation relevance. This work underscores the user intention dimension in the explicit-implicit feedback dichotomy and offers insights for designing personalized recommendation feedback that better responds to users' needs.

Figures

Figures reproduced from arXiv: 2502.09869 by the authors.

Figure 1
Figure 1. The main user interfaces of Xiaohongshu and Douyin. ( [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The main user interfaces of Kuaishou and Bilibili Shorts. ( [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.