diff --git a/dlibrary/dlibrary.py b/dlibrary/dlibrary.py
index 096a623..88823e7 100755
--- a/dlibrary/dlibrary.py
+++ b/dlibrary/dlibrary.py
@@ -7,6 +7,7 @@ from io import BytesIO
 from pathlib import Path
 import os
 from os.path import relpath, splitext
+import random
 import re
 import readline
 import shutil
@@ -91,6 +92,8 @@ MULTIPART_RAR_TAIL_REGEX = re.compile(r'^(.+)\.part0*([^1]|[^0].+)\.rar$', re.I)
 PDF_REFERENCED_IMAGE_REGEX = re.compile(r'(^|(?<=\s))/(?P<ref_name>\S+)\s+Do($|(?=\s))')
 PDF_INLINE_IMAGE_REGEX = re.compile(r'(^|\s)(BI|ID|EI)($|\s)')
 
+SUGGESTED_WORKS_COUNT = 10
+
 debug_mode = False
 def debug(s):
     if debug_mode:
@@ -1012,18 +1015,23 @@ def similarity(a, b):
     memoized_similarities[(shorter, longer)] = result
     return result
 
-def top(items, n, key):
+def top(items, n, key, overflow=0):
     winners = []
     for item in items:
         score = key(item)
-        if len(winners) < n or score > winners[-1][1]:
+        if len(winners) < n or score >= winners[-1][1]:
             for i in range(len(winners) + 1):
-                if i == len(winners) or score > winners[i][1]:
+                if i == len(winners) or score >= winners[i][1]:
                     winners.insert(i, (item, score))
                     break
-            while len(winners) > n:
+            while len(winners) > n and winners[-1][1] < winners[n-1][1]:
                 winners.pop()
-    return [item for (item, score) in winners]
+
+    # shuffle followed by stable sort to randomly shuffle within each score tier
+    random.shuffle(winners)
+    winners.sort(key=lambda w: w[1], reverse=True)
+
+    return [item for (item, score) in winners[:n+overflow]]
 
 def generate(args):
     jenv = Environment(
@@ -1080,7 +1088,7 @@ def generate(args):
             if work['series'] and work['series'] == other_work['series']:
                 return -1
             return similarity(work['title'], other_work['title'])
-        suggested = top(works, 6, suggestion_priority)
+        suggested = top(works, SUGGESTED_WORKS_COUNT, suggestion_priority)
 
         work_dir = site_dir / 'works' / work['id']
         viewer_dir = work_dir / 'view'