def addRating():
print("Enter userId,bookId,rating seperated by spaces")
inps = list(map(int, input().split()))
try:
cbr.getUserDetail(inps[0])
cbr.getItemDetail(inps[1])
except:
print("Invalid userId or itemId")
return
try:
cbr.addRating(inps[0], inps[1], inps[2])
except:
print("Mismatch in no. of input values")
def searchForItems(searchTerm,
userId,
attributes=[
'title', 'author', 'pub-year', 'publisher', 'category',
'description'
]):
hybridItems = getHybridSimilarItemsForAUser(userId, 1000)
hybridItemsExtended = []
for hybridItem in hybridItems:
id, simScore = hybridItem[0], hybridItem[1]
item = cbr.getItemDetail(id)
text = ""
for attribute in attributes:
text += str(item[attribute])
text = text.lower()
searchScore = getScoreForText(text, searchTerm)
hybridItemsExtended.append((id, searchScore, simScore))
hybridItemsExtended.sort(key=lambda x: (x[1], x[2]), reverse=True)
print(hybridItemsExtended[:20])
return hybridItemsExtended
def getItemDetail():
try:
print("Enter ItemId ")
itemId = int(input())
paintDict("Item Info", cbr.getItemDetail(itemId))
except:
print("Invalid ItemId")
return
def processQueryByItemId():
print("Enter ItemId ")
try:
itemId = int(input())
paintDict("Item Info", cbr.getItemDetail(itemId))
CBRItems = getCBRSimilarItemsForAItem(itemId)
CFRItems = getCFRSimilarItemsForAItem(itemId)
HybridItems = getHybridSimilarItemsForAItem(itemId)
#computeMetrics(CBRItems,CFRItems,HybridItems,["category","author"])
paint("CBR Items", CBRItems, printableMatterIndex=1)
paint("CFR Items", CFRItems)
paint("Hybrid Items", HybridItems)
except:
print("Invalid ItemId")
return
def getVector(Items, Attributes, title):
vector = {}
for itemId, score in Items:
try:
item = cbr.getItemDetail(itemId)
except:
continue
for attribute in Attributes:
entity = item[attribute]
if entity not in vector:
vector[entity] = 0
vector[entity] += 1
print()
printFocusPoints(vector, title)
return vector