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)