Game List Guide
The relationship between human game curation, whether from critics, reviewers, or community lists, and algorithmically generated recommendation, introduced in more detail in this site's dedicated article on how algorithms recommend games, has generated genuine debate and no shortage of oversimplified claims on both sides. Here's a myth-versus-fact breakdown worth understanding.
Fact: Despite algorithmic recommendation's genuine growth and sophistication, human game curation, whether through professional reviews, community-built lists, or content creators, remains a significant, actively consulted part of how many players make purchasing and playing decisions, and many players continue to actively seek out and value this human perspective specifically, rather than exclusively relying on algorithmic recommendation. The two approaches have settled into more of a coexisting, complementary relationship than a straightforward displacement of one by the other.
Fact: While human curators genuinely excel at certain kinds of discovery, particularly more unusual, boundary-pushing games requiring genuine qualitative judgment and design context, algorithmic systems have real, demonstrated strengths of their own, particularly in efficiently surfacing games with statistical similarity to a player's established taste at a scale and speed no individual human curator could realistically match. Each approach has genuine comparative strengths rather than one being straightforwardly superior across every discovery dimension.
Fact: This is a genuinely important misconception worth correcting directly. Algorithmic recommendation systems are built and trained on specific data and design choices made by human engineers and companies, meaning they can and do reflect meaningful biases, including potential biases toward more commercially prominent games, and biases embedded in whatever historical player data the system was trained on, alongside the genuine commercial incentives discussed in this site's article on algorithmic game recommendation. Algorithmic curation isn't inherently more "neutral" or "objective" than human curation, it simply reflects a different, less immediately visible kind of curatorial influence and potential bias.
Fact: This actually tends to run in the opposite direction in most practical cases. Algorithmic recommendations are frequently more individually personalized than human-curated lists, precisely because they draw directly on your own specific play data, while human-curated lists, unless created specifically with your individual taste in mind, are generally built for a broader audience or a general thematic purpose rather than calibrated to any one individual player's specific taste profile.
Fact: There's no genuine requirement to exclusively favor one approach over the other, and many of the most gaming-engaged players draw on both approaches for different purposes, algorithmic recommendations for efficient, low-effort discovery aligned with established taste, and human-curated lists, along with their own personal backlog-building discussed throughout this site, for more deliberate, actively engaged game exploration and critical context.
Fact: While it's reasonable to expect algorithmic recommendation to continue growing more sophisticated over time, there's no strong evidence currently suggesting a complete displacement of human curation is either inevitable or imminent, particularly given that many players report genuinely valuing human curatorial perspective and community discussion specifically, a preference that reflects something more than pure recommendation accuracy or efficiency alone.
Fact: It's worth acknowledging honestly that aggregated review scores, while genuinely useful and considerably more contextually informed than pure algorithmic pattern-matching, aren't themselves a fully objective, unbiased measure of a game's quality either, aggregated scores can reflect their own patterns of bias and blind spot, including genre-based scoring tendencies and historical underrepresentation of certain design traditions, meaning review-based curation should also be engaged with thoughtfully rather than treated as an infallible, purely objective final word.
Human curation and algorithmic recommendation represent two genuinely different approaches with real, distinct comparative strengths and limitations, rather than a simple hierarchy where one approach is straightforwardly superior to the other across every relevant dimension. Understanding these genuine, specific trade-offs, rather than relying on the oversimplified claims that frequently circulate around this comparison, equips you to more thoughtfully and intentionally draw on both approaches in a way that genuinely serves your own gaming taste and discovery goals.