Any ratings or classification system has some very fundamental challenges.
There's no way to collapse a multidimensional metric to a single value without losing information. That's what a "star system", numeric review, or others, do. Quick, what are food grades, and what are the grading criteria? (See: http://articles.latimes.com/1987-11-13/business/fi-13882_1_m... and https://www.princeton.edu/~ota/disk3/1977/7707/770706.PDF)
Different people have different assessment criteria. That seems to be the problem the hotel in question claims to be facing. "What's best?" inherently presumes "For what?". A vintage hotel shouldn't be rated on the same criteria as a five-star downtown hotel or business hotel. Or for its healthcare, auto-repair, clothing, bookstore, or hardware characteristics. The challenge is when an entity tries to claim a special status or twist that's at strong odds to most general expectations.
Thin data. Only a small number of people typically leave reviews, and you're likely to get feedback only for exceptionally positive, or exceptionally negative, experiences. Or those who are motivated for whatever reasons (competition trying to shut you down, paid positive reviewers, etc.) to tell a specific story. This produces a highly non-randomized sampling selection.
Unqualified reviewers. This is a problem pretty much any collectively rated system has. People who are not capable of making a valid assessment. Among other services, reddit and HN are suffering from this.
The significance of ratings. Meaningless as they are, small changes in ratings can be hugely significant for a business. A major problem of the market is that information on quality is thin, non-abundant, and often expensive to come by. Ratings systems, even imperfect ones, address the ratings cost issue, even if the data conveyed are low-quality (think of racial discrimination or similar behaviors). So despite the problems, people care.
One way around these issues is to come up with a "based on your profile you'd likely prefer X". This is what Amazon tries to accomplish with its product recommendations, though the appropriateness of these is often pretty thin.
The problem space is a hard one.