Comparing store performance with bayesian average rating

Question

I'm trying to compare the performance of stores based on their ratings and reviews and I came across two approaches - weighted rating and Bayesian rating in a similar post What is a better way to sort by a 5 star rating?

My dataset is somewhat similar where the stores have their overall ratings (out of 5 star ratings) and no of reviews. However, some stores have higher ratings with less reviews, some have higher ratings and higher reviews, and others have less ratings with higher reviews. I have some difficulty understanding what does 'm' mean in the weighted rating method, which is weighted rating = (v / (v + m)) * R + (m / (v + m)) * C as well as Evan Miller's Bayesian formula, which is ]1

nk is the number of k-star ratings,
sk is the "worth" (in points) of k stars,
N is the total number of votes
K is the maximum number of stars (e.g. K=5, in a 5-star rating system)
z_alpha/2 is the 1 - alpha/2 quantile of a normal distribution. If you want 95% confidence (based on the Bayesian posterior distribution) that the actual sort criterion is at least as big as the computed sort criterion, choose z_alpha/2 = 1.65```

Below is a sample dataset to provide more clarity. The ratings lie between 3.5 to 4.6 with reviews ranging from ~200 to ~2800. Which of the above two methods should be a good fit in my case and how can I use the variables in my dataset in the above two formulae?

Store	Ratings	No of Reviews
101	3.7	211
102	3.6	1,194
103	3.7	1,879
104	3.7	876
105	3.7	765
106	3.7	922
107	3.5	502
108	3.7	2,401
109	3.9	635
110	3.9	505
111	3.8	275
112	3.9	1,021
113	3.9	1,931
114	4	851
115	4.1	741
116	4.1	749
117	4	500
118	4.2	896
119	4.2	2,807
120	4.2	1,372
121	4.1	1,807
122	4.2	2,526
123	4	1,170
124	4.2	1,587
125	4.2	2,125
126	4.1	1,959
127	4.3	862
128	4.3	1,249
129	4.4	2,143
130	4.4	1,396
131	4.4	366
132	4.4	954
133	4.5	1,058
134	4.5	230
135	4.6	436
136	4.6	1,000