def Item_based():
user_id=session['user_id'][0]
productData = functions.flipPersonToPlaces(reviewdata1.reviews)
print "Finding similar Places " # for single place
similar_item=functions.mostSimilar(productData,"4iTRjN_uAdAb7_YZDVHJdg")
print dict(similar_item)
b_data=[]
for bid in dict(similar_item).keys():
sql = ("SELECT B_NAME, PHOTO_URL,RATING from BUSINESS_CA where B_ID = '%s'" % bid)
cursor.execute(sql)
data = cursor.fetchone()
if data != None:
b_data.append([data[0],data[1],data[2]])
print b_data
print "******************"
print "Computing Item Similarity" # for entire product data
itemSimilarity = functions.computeItemSimilarities(productData)
print itemSimilarity
print "******************"
print "Item Based Filtering for Recommendations"
recommendedplc_ib=functions.itemBasedFiltering(reviewdata1.reviews,str(user_id),itemSimilarity)
print recommendedplc_ib.keys() #send to shivani
locations =[]
for bid in recommendedplc_ib.keys():
sql = ("SELECT B_NAME, LATITUDE, LONGITUDE, ADDRESS, RATING from BUSINESS_CA where B_ID = '%s'" % bid)
cursor.execute(sql)
data_map = cursor.fetchone()
if data_map!=None:
value=[str(data_map[0]),float(data_map[1]),float(data_map[2]),str(data_map[3]),data_map[4]]
locations.append(value)
print "locations is"
print locations
return render_template('ItemRecommend.html',location=locations,similarplaces=b_data)
# print "Most similar critcs " # print functions.mostSimilar(data.critics,"Toby") # print " " # print "Product Recommendations for a user" # print functions.getRecommendations(data.critics,person2) #how much will one user like a particular movie # print " " productData = functions.flipPersonToMovie(data.critics) # print "Finding similar movies " # print functions.mostSimilar(productData,"Superman Returns") #Find similar movies # print " " # print "Finding user Recommendations for a product" # print functions.getRecommendations(productData,"Just My Luck") #Out of the people Who havent seen the movie Who will like this movie ? # print " " # print "Computing Item Similarity" itemSimilarity = functions.computeItemSimilarities(productData) print "Item Based Filtering for Recommendations" print functions.itemBasedFiltering(data.critics, "Toby", itemSimilarity)
