--- title: "Why Do Match Scores Feel Untrustworthy? How to Evaluate Networking Recommendations" description: "Why do match scores feel untrustworthy? A high percentage can look precise while revealing very little about why two people should meet. This guide explains the limits of opaque networking scores, the signals that make recommendations credible, and how contextual, explainable matching can help people decide who is actually worth meeting." canonical: "https://meetwho.app/blog/why-match-scores-feel-untrustworthy" language: "en" published: "2026-08-21T12:57:10.884+00:00" updated: "2026-08-21T12:57:11.253741+00:00" reading_time_minutes: "19" source: "MeetWho — the networking layer for events and communities" license: "Quote with attribution and a link to the canonical URL." --- # Why Do Match Scores Feel Untrustworthy? How to Evaluate Networking Recommendations ## TL;DR - A match score compresses many possible signals and assumptions into a single output. - Numbers such as 87%, 91%, or 96% appear highly specific. - Compatibility is always compatibility for something. - A networking match score is typically a system-generated indicator intended to represent some form of compatibility or relevance between people. - Similarity is easy to understand: two people may share an industry, profession, location, interest, or background. ## Key questions **Why Do Match Scores Feel Untrustworthy in the First Place?** A match score compresses many possible signals and assumptions into a single output. Depending on the platform, those signals could include profile information, interests, preferences, goals, behaviour, or contextual information. **What Does a Networking Match Score Actually Measure?** A networking match score is typically a system-generated indicator intended to represent some form of compatibility or relevance between people. The difficulty is that “match score” has no universal definition . **6 Reasons a Match Percentage Can Be Hard to Trust** A match percentage is not automatically useless. The problem is that users are often expected to interpret it without seeing enough of the reasoning behind it. **What Makes a Networking Recommendation Trustworthy?** A trustworthy recommendation does not need to reveal every technical detail of an algorithm. Most users do not need to inspect model architecture or weighting formulas before deciding whether to meet someone. **Context: Does It Understand Why You Are at This Event?** A strong networking recommendation should reflect the situation in which the connection is being made. The same two people may be relevant at one event and less relevant at another. **Reasons: Can You Understand Why This Person Was Suggested?** The user should be able to see more than an unexplained ranking. Useful reasons might include a shared professional interest, a complementary need, a relevant event goal, or another clearly stated point of connection. ## Full article Title: "Why Do Match Scores Feel Untrustworthy? Explained" Description: "Why do match scores feel untrustworthy? Learn what makes networking recommendations credible, explainable, contextual, and genuinely useful before you connect." # Why Do Match Scores Feel Untrustworthy? How to Evaluate Networking Recommendations **Why do match scores feel untrustworthy?** A percentage can look scientific without telling you what was measured, how important each signal was, or whether the result reflects what you actually want from a conversation. In professional networking, a useful recommendation needs more than apparent precision: it needs context, understandable reasons, mutual relevance, and enough information for you to make the final decision yourself. A “92% match” feels reassuring because numbers suggest measurement. But the number alone does not tell you whether two people share useful professional goals, have complementary needs, are relevant to each other at this particular event, or would even have something meaningful to discuss. **Match scores often feel untrustworthy because a precise percentage does not explain what was measured, how different signals were weighted, or what the match is intended to predict.** In professional networking, people usually need more context: why someone is relevant, what connects them, how they might help each other, and whether the recommendation fits what they want from the event. ## Why Do Match Scores Feel Untrustworthy in the First Place? A match score compresses many possible signals and assumptions into a single output. Depending on the platform, those signals could include profile information, interests, preferences, goals, behaviour, or contextual information. But there is no universal formula behind a “match percentage,” which means a score cannot be interpreted reliably unless the product explains what it represents. Imagine two attendees are shown as an 89% match. Does that mean they work in the same industry? Want the same type of introduction? Have complementary skills? Mentioned similar interests? Or does the system predict that they are likely to have a valuable conversation? Without an explanation, the percentage answers none of those questions. That is the core problem with an opaque **match score**: it can communicate ranking without communicating meaning. A user may know that Person A ranks higher than Person B, but still have no idea why either person deserves ten minutes of their limited networking time. ### A Precise Number Can Create an Illusion of Certainty Numbers such as 87%, 91%, or 96% appear highly specific. That visual precision can make an output seem more certain than it really is. There is an important distinction between **precision of presentation** and certainty about an outcome. A system may calculate a score consistently according to its own rules while still being unable to guarantee that two people will have a useful conversation. Professional chemistry, timing, changing priorities, personal judgement, and incomplete profile information cannot always be reduced to a single percentage. This does not mean numerical matching is inherently misleading. Scores can be useful for ranking or filtering when users understand their purpose. The trust problem appears when the number is presented without enough context to evaluate it. A better recommendation therefore helps the user understand the reasoning rather than expecting the number itself to carry the explanation. ### The Missing Question Is Usually “Matched for What?” Compatibility is always compatibility for something. Two founders working in the same industry may look highly similar on paper, yet neither may have a reason to meet. One could be looking for distribution partners while the other wants to hire an engineer. Their profiles overlap, but their immediate networking objectives do not. Now consider two people with less obvious similarity. One is building an early-stage company and looking for specialist expertise. Another has exactly that expertise and is interested in advising young companies. Their job titles, industries, and profile keywords might not produce maximum similarity, but the potential conversation could be considerably more relevant. That is why **networking relevance** depends on purpose, not merely resemblance. The useful question is not simply, “How similar are these people?” It is, “Why might these two people benefit from meeting now?” ## What Does a Networking Match Score Actually Measure? A networking match score is typically a system-generated indicator intended to represent some form of compatibility or relevance between people. The difficulty is that **“match score” has no universal definition**. A 90% score on one platform may represent something completely different from a 90% score on another. One system might emphasise shared interests. Another might prioritise professional roles or stated preferences. A different recommendation system might combine several signals and rank people relative to the available attendee pool. Unless the methodology is explained, users should not assume that two percentages from different products—or even two different contexts—mean the same thing. The interpretation also depends on what the system is trying to optimise. Is it looking for similarity, complementary needs, likely engagement, common interests, event-specific relevance, or some combination of those factors? A number becomes far more useful when that objective is visible. ### Similarity Is Not the Same as Relevance Similarity is easy to understand: two people may share an industry, profession, location, interest, or background. But professional networking is often valuable precisely because people bring **different but complementary resources** to the conversation. Suppose one attendee is seeking a technical co-founder and another wants to join an early-stage company. They may share fewer profile terms than two founders working in the same sector, yet their objectives could make the first pairing substantially more relevant. This is why an effective **networking recommendation** should not be judged only by how much two profiles resemble each other. What matters is whether the available context gives both people a credible reason to start a conversation. ### Relevance Is Not the Same as Mutual Value Relevance can still be one-sided. A person may be highly useful to you without having any clear reason to spend time with you. That matters in professional networking, because a recommendation that benefits only one side can create awkward outreach and low-quality interactions. Mutual value means there is a plausible reason for both people to engage. One attendee may have expertise another needs, while the second may offer access, perspective, feedback, introductions, or domain knowledge in return. The value does not need to be equal in a measurable sense, but the recommendation should make a reasonable case for why the conversation could be worthwhile for both sides. This is where a single **compatibility score** often becomes too compressed. A percentage might tell you that two people rank highly according to a system, but it does not necessarily reveal whether the relationship is based on similarity, complementarity, shared goals, or a one-sided opportunity. ## 6 Reasons a Match Percentage Can Be Hard to Trust A match percentage is not automatically useless. The problem is that users are often expected to interpret it without seeing enough of the reasoning behind it. Several recurring issues make these scores difficult to evaluate. ### 1. You Cannot See the Signals Behind the Score A score may be based on profile fields, interests, goals, behavioural data, preferences, or other inputs. If the platform does not explain which signals matter, the user cannot tell what the number actually represents. That creates a transparency gap. A person may look like a strong recommendation, but the user cannot distinguish between a genuinely relevant connection and a result driven by superficial overlap. ### 2. You Do Not Know How Those Signals Were Weighted Even when users know which data points are considered, weighting still matters. A system could treat shared industry as more important than networking intent. Another might prioritise stated goals over job titles. A third could combine several factors dynamically. Two platforms might use the same profile information and still rank the same people very differently. Without some indication of why a person was prioritised, the final percentage can feel arbitrary even when the calculation itself is consistent. ### 3. Your Current Goal May Be Missing Professional networking intent is not fixed. At one event, someone may be looking for investors. At another, the same person may be trying to recruit talent, find distribution partners, meet peers, or simply learn about a new market. A static profile can therefore miss what matters most in the current context. This is why event-specific relevance is especially important. A recommendation may be reasonable in general but poorly timed for the meeting, conference, workshop, or community event the person is attending now. ### 4. Profile Data Can Be Incomplete or Outdated Recommendation systems can only work with the information available to them. A profile that has not been updated for a year may still describe an old role, old priorities, or interests that are no longer important. A sparse profile creates a different problem: the system may have too little context to distinguish between genuinely useful connections and weak similarities. That does not mean incomplete profiles make recommendations worthless. It means users should understand that recommendation quality depends partly on the quality and recency of the inputs. ### 5. A Single Number Hides Trade-Offs Two recommendations can receive similar scores for completely different reasons. One person might share your industry and professional background but have little overlap with your immediate goals. Another may work in a different field but offer exactly the expertise, access, or perspective you need. If both are represented by a single number, those differences disappear. An **explainable recommendation** makes the trade-offs visible and gives the user something concrete to judge. ### 6. The Score Does Not Tell You What to Do Next Ultimately, networking is an action problem. You need to decide whether to approach someone, accept a connection request, start a conversation, or spend limited event time elsewhere. A bare score rarely answers the practical question behind that decision: **Why should I speak to this person?** If a recommendation cannot help answer that question, even a highly precise percentage may create more curiosity than confidence. ## What Makes a Networking Recommendation Trustworthy? A trustworthy recommendation does not need to reveal every technical detail of an algorithm. Most users do not need to inspect model architecture or weighting formulas before deciding whether to meet someone. What they do need is enough context to understand the recommendation and make their own decision. A useful framework is **Context + Reasons + Mutuality + Control + Privacy**. Together, these five elements make recommendations easier to evaluate without pretending that any system can guarantee a successful conversation. ### Context: Does It Understand Why You Are at This Event? A strong networking recommendation should reflect the situation in which the connection is being made. The same two people may be relevant at one event and less relevant at another. Event goals, current professional priorities, and the type of conversation someone wants can all change which introductions are worth making. ### Reasons: Can You Understand Why This Person Was Suggested? The user should be able to see more than an unexplained ranking. Useful reasons might include a shared professional interest, a complementary need, a relevant event goal, or another clearly stated point of connection. The explanation does not need to expose proprietary logic; it needs to make the recommendation understandable enough to assess. ### Mutuality: Is There Potential Value for Both People? The best professional introductions are not simply lists of people who can help you. A more useful recommendation considers whether both participants have a plausible reason to engage. That makes outreach more respectful and can lead to more meaningful conversations than one-sided prospecting. ### Control: Can You Decide Whether to Act? Even a highly relevant recommendation should remain a recommendation, not a command. In professional networking, the user should retain the ability to decide whether a suggested introduction makes sense. A system can reduce the effort required to identify promising people, but it cannot fully understand interpersonal chemistry, timing, personal comfort, or every nuance of a professional relationship. That distinction matters for trust. When users can inspect the reasons, disagree with them, and choose whether to connect, AI becomes a decision-support layer rather than an authority. ### Privacy: Is Matching Limited by Consent and Visibility Rules? A networking system should not become more useful by becoming less respectful of privacy. Trustworthy recommendations should operate within the visibility choices and consent settings available to participants. A recommendation should not imply that hidden profiles, private contact information, or restricted attendee data become accessible simply because a matching system considers someone relevant. Privacy therefore belongs inside the recommendation model itself. Users should know that being suggested to someone does not automatically remove their control over whether and how a connection takes place. ## Match Score vs Explainable Recommendation A score and an explanation are not necessarily competing ideas. A percentage can still be useful when its purpose is clear and when users understand what it represents. The problem begins when the number becomes the entire recommendation. An **explainable recommendation** gives users enough context to evaluate the suggestion. Instead of asking them to trust “91%,” it helps answer why the person is relevant, what they may have in common, where complementary value exists, and what kind of conversation could follow. Question Match Score Alone Explainable Recommendation Why was this person suggested? Often unclear Gives specific reasons What should I discuss? Usually unknown Can identify relevant themes Does the event goal matter? Not visible from the score Can be reflected in the rationale Is the value mutual? Difficult to infer Can explain potential value for both sides Can I challenge the recommendation? Hard without context Easier because the reasoning is visible Does a high number guarantee chemistry? No No Who makes the final decision? The user should The user should Explainability does not make a recommendation objectively correct. It makes the recommendation easier to inspect, question, compare, and act on. ## How to Evaluate a Match Before You Reach Out A networking recommendation becomes useful when it helps you make a better decision, not merely when it produces a higher number. Before sending a connection request or walking across a conference hall to introduce yourself, look at the person, the purpose, and the potential conversation. The following checklist provides a practical way to evaluate whether a recommendation deserves your attention. ### Check the Person, the Purpose, and the Potential Conversation - I understand why this person was recommended. - The recommendation relates to my current networking goal. - I can identify potential value for both of us. - The explanation is more useful than the percentage alone. - I know what I could talk to this person about. - I still control whether to send or accept a connection request. - The recommendation respects visibility and consent settings. - I would consider the introduction even without seeing the score. The final question is especially useful. If removing the percentage also removes your reason to meet the person, the recommendation probably needs more context. #### A Simple Trust Test > If removing the match percentage leaves you with no reason to contact the person, the recommendation probably needs more explanation. This is not a scientific rule. It is a practical way to separate numerical persuasion from genuinely useful decision context. ## Should AI Tell You Who to Meet? AI can help narrow a large attendee pool and surface potentially relevant people, but it should not make the final networking decision for you. The more consequential the recommendation, the more useful it is for the system to explain the context and reasons behind it. At a conference with hundreds of attendees, manually scanning every profile may be unrealistic. AI can assist by filtering information, identifying relationships between stated goals and interests, ranking potentially relevant people, and summarising why an introduction might make sense. The human role is different. You decide whether the context is still accurate, whether the suggested value feels realistic, whether the timing is right, and whether you actually want to connect. ### Good AI Reduces Search Effort Without Removing Human Choice The strongest use of AI in networking is not to declare who your “best match” is with false certainty. It is to reduce search effort while preserving judgement. A recommendation system may notice that one attendee is looking for expertise another participant can provide, or that two people have overlapping event objectives. That signal can be useful, but it still needs interpretation by the people involved. ### Explanations Make Recommendations Easier to Question Trust should not mean accepting every recommendation. A transparent explanation gives users a basis for disagreement. You might recognise that a stated goal is outdated, decide that the suggested connection is relevant but not at this event, or realise that a person is more useful to you than you are to them. That ability to challenge the recommendation is a feature, not a failure. **Explainable recommendations** are valuable because they help users understand why a suggestion exists while leaving room for human judgement. ## How MeetWho Approaches Event Networking Recommendations MeetWho is built around the idea that event networking should help people understand **who may be worth meeting and why**, rather than simply presenting a public attendee list and expecting participants to search through it themselves. Participants can create professional profiles that describe what they are working on, what they are looking for, whom they want to meet, and where they may be able to help others. MeetWho can analyse this information alongside event goals and shared interests to surface ranked recommendations among users who have permitted networking. ### Recommendations Are Built Around Networking Context MeetWho’s networking model starts with information participants choose to provide about their professional context. They can describe what they are working on, what they are looking for, the types of people they want to meet, and the areas where they may be able to help others. That information can be considered alongside event goals and shared interests to identify potentially relevant people. The important point is not a hidden percentage. It is whether the recommendation gives the participant enough context to understand why the introduction may make sense. ### The Recommendation Explains Why the Introduction May Matter MeetWho can show why two people may want to meet, how they could potentially help one another, and how a conversation could begin. That approach addresses one of the central weaknesses of an unexplained **networking match score**. Instead of asking participants to trust a number, the recommendation gives them reasons they can inspect for themselves. A useful explanation might reveal a shared interest, complementary objective, or professional connection that would otherwise be difficult to spot in a large attendee pool. The participant can then decide whether that reason is strong enough to justify reaching out. ### The Participant Still Controls the Connection A recommendation does not automatically create a relationship. MeetWho users can send a connection request, and messaging becomes available when both participants connect. Users can also keep private notes, create follow-up reminders, and manage their connection history after an event. This preserves an important boundary between discovery and decision. Technology can help identify relevant people, but participants still decide whom they want to contact and whether the conversation should continue. ### Privacy Is Part of the Matching Model Networking relevance should never override participant consent. MeetWho prioritises organizer settings and participant permission when determining networking visibility. Paid membership does not unlock hidden profiles or private contact information, and MeetWho does not sell attendee lists. That matters because recommendation quality and privacy are not opposing goals. A system can help people discover meaningful professional connections while still respecting who has chosen to participate and what information is available. **Prefer context over a mystery percentage.** MeetWho is designed to help participants understand who may be worth meeting and why, while keeping networking decisions in their hands. ## What Event Organizers Should Look for in Networking Software For organizers, the most useful question is not simply whether a platform offers “AI matching.” The better question is what participants actually see and whether those recommendations help them make better networking decisions. Recommendation quantity can look impressive, but volume alone does not create useful conversations. A system that generates dozens of unexplained matches may place more interpretation work on participants than one that surfaces fewer, more contextual recommendations. ### Recommendation Quality Matters More Than Recommendation Quantity Event networking works under time constraints. A participant may have hundreds of possible people to meet but only enough time for a handful of meaningful conversations. That makes prioritisation more valuable than abundance. MeetWho’s “Know who to meet” approach reflects this idea: the goal is not to maximise the number of introductions but to help participants identify people with a plausible reason to talk. ### Ask Vendors How Recommendations Are Presented to Attendees When comparing networking platforms, organizers should ask practical questions: - Do attendees see why someone was recommended? - Can participants express their current networking goals? - Is potential value considered from both sides? - Can organizers control networking privacy settings? - Does participant consent determine networking visibility? - Are users expected to search a public attendee directory? - Does upgrading reveal private profiles or contact details? - What information is actually used to create recommendations? Organizers should also ask what role AI plays, whether participants remain in control of connection decisions, and how users can update information that may no longer reflect their goals. Running an event? MeetWho combines event creation, registration, attendee management, privacy-controlled networking, reminders, QR check-in, and intelligent introductions in one platform. **Create an event for free with MeetWho.** ## A Better Question Than “What Is My Match Score?” Instead of asking only, “How high is the score?”, ask whether you have enough information to make a useful decision. Why is this person relevant to me? Why might I be relevant to them? What could we realistically discuss? Does the recommendation reflect what I want from this event? Would the introduction still make sense if the percentage disappeared? These questions move networking away from passive score-watching and toward informed choice. A useful recommendation should help you understand **who to meet, why the meeting could matter, and how to start the conversation**. ### Useful Networking Recommendations Should Lead to Better Conversations The real value of a recommendation appears after the ranking is generated. If it helps someone identify a relevant person, understand the potential mutual value, and begin a more focused conversation, the recommendation has supported the decision. If it provides only a percentage with no context, the user is still left to solve the hardest part alone. ## Frequently Asked Questions About Match Scores ### What does a match score mean? A match score is a system-generated indicator intended to represent some form of compatibility or relevance. There is no universal definition. Different platforms may use different signals, priorities, and methods, so a 90% score on one service may not mean the same thing as 90% on another. ### Are match percentages accurate? A match percentage may be calculated consistently within a particular system, but that does not mean it can predict the quality of a conversation with certainty. Its usefulness depends on what the score measures, the quality of the underlying information, and whether the result reflects the user’s actual networking goal. ### Why do match scores feel untrustworthy? **Match scores feel untrustworthy** when users see a precise number without understanding what created it. If the signals, context, purpose, or recommendation rationale are unclear, the percentage can feel more authoritative than informative. ### Is a 90% match necessarily better than an 80% match? Not necessarily. Without knowing what the score represents and how the system ranks people, a ten-point difference may not tell you which conversation will be more valuable. Context and relevance matter more than the number alone. ### Can AI predict whether two people will have a good conversation? AI can identify patterns and potentially relevant connections based on the information available to it, but it cannot guarantee interpersonal chemistry or conversation quality. It is most useful as a discovery and decision-support tool rather than a substitute for human judgement. ### What is an explainable networking recommendation? An explainable networking recommendation is a suggestion accompanied by understandable reasons for why two people may be relevant to one another. It can highlight shared context, complementary goals, possible mutual value, or useful conversation themes so users can judge the recommendation themselves. ### Should networking platforms show match scores? They can, provided the purpose and meaning of the score are clear. A number can help with ranking or prioritisation, but it should not replace context. Users still need to understand why a person is being recommended and whether the introduction fits their goals. ### How does MeetWho help people decide who to meet? MeetWho provides ranked recommendations among participants who have permitted networking. It can use participant-provided information, event goals, and shared interests to explain why two people may want to meet, how they could help each other, and how a conversation might begin. The next time an app tells you someone is a “94% match,” the percentage should not be the end of the explanation. The better questions are what connects you, why the introduction matters now, whether the value can run both ways, and whether you have enough context to choose for yourself. With MeetWho, the focus is not on meeting as many people as possible. It is on helping participants understand **who may be worth meeting and why**. **Know who to meet with MeetWho — or create your event for free.** --- Canonical HTML version: https://meetwho.app/blog/why-match-scores-feel-untrustworthy Machine-readable site index: https://meetwho.app/llms.txt