VIMIAC10代做、代写Java代写、recommender system代写、Java程序设计调试

日期：2019-11-03 09:31

VIMIAC10 2019 2nd Major homework

Audiobook recommender system

1. Introduction

Let us assume, that there is an online audiobook store that displays common

properties for each audiobook offered for sale. Such properties include the ratings

given by previous buyers, a summary, name of the author, narrator, and length. Users

can rate audiobooks on a five-point scale. Based on these reviews, the system

recommends additional audiobooks to each user.

2. Assignment

Implement a recommendation algorithm in Java or Python! The algorithm can be

chosen freely, but you are not allowed to use any code that you yourself have not

written. The only exception in case of a Java implementation is the Apache Commons

library (http://commons.apache.org ), which can be used.

2.1. Java

The code must contain a Main class, and within this, a main() function. It will receive

all inputs on the standard input, and should output the solution to the standard output.

Upload the zipped source code files of your application to the BME MIT HomeWork

portal. (https://hf.mit.bme.hu).

2.2. Python

The code must be a single python file, that will be run and receives all inputs onto the

standard input, and it should write the solution to the standard output. Upload the

zipped single python file to the BME MIT Homework portal. Use Python3.x, and only

standard libraries are available (e.g. no numpy!) (https://hf.mit.bme.hu).

2.3. Input

The first line of input contains the number of known ratings, users and audiobooks.

Then each line contains a user ID, an audiobook ID, and the corresponding rating. The

fields are separated by a tab. Example:

60000 500 200

0 3 4

0 31 3

VIMIAC10 2019 2nd Major homework

0 87 2

...

499 192 5

499 198 4

2.4. Output

The output contains the IDs of the top 10 recommended audiobooks for each user,

separated by tabs.

Audiobook Identifiers should be ordered such that the top of the list, i.e. the most

recommended audiobook, comes first. However, the list should not include an

audiobook whose rating is known (already given by that user)! The rows should follow

each other in the order of user IDs. These IDs should not be displayed.

In case of the example above, the output contains 500 lines:

175 21 76 77 119 2 40 42 56 117

32 142 38 111 14 18 101 138 64 29

75 88 47 43 18 150 83 124 166 182

3. Evaluation

The rating is based on the number of users for which there is an overlap between the

list of top n recommended audiobooks and the list of actually top rated audiobooks.