UNIVERSITY OF TORONTO FACULTY OF APPLIED SCIENCE AND ENGINEERING. FINAL EXAMINATION, December, DURATION: 2 and½ hrs

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1 UNIVERSITY OF TORONTO FACULTY OF APPLIED SCIENCE AND ENGINEERING FINAL EXAMINATION, December, 2017 DURATION: 2 and½ hrs MIE451 / Decision Support Systems Exam Type: X Examiner - S. Sanner Last name First name UTORid Student number U oft Instructions: 20 points total, 8 questions, open book and notes, no electronic devices. Full credit will be given for succinct and justified explanations of answers - try to limit explanations to 1-2 sentences. Unreasonably long explanations or those with irrelevant information will not receive full credit. Many questions have multiple valid answers. Please provide answers in the space provided; use space at the end if absolutely necessary ( clearly label the question # and indicate in the provided question space where the question is answered, if elsewhere).

2 ' 1. [2pts] Answer the following: (a) [lpt] Provide one reason why it is difficult to use machine learning for information retrieval. (b) [lpt] Provide one reason why it is difficult to use recommender systems for information retrieval. This should be different from your answer above for machine learning. 2. [2pts] You are building a tool to analyze conversation content between two Facebook users to predict if they are immediate family members (i.e., husband/wife, parent/child, but you don't need to predict the exact family relation). You can assume there are tools to support all sentiment analysis in the lecture slides. Explain how to heuristically rank all pairs of users who converse, from most to least likely to be immediate family members. Do not provide a list of individual words or phrases like "hi honey''. Do not assume you have labeled training data. 3. [2pts] From a photo sharing website, you collect tags of people appearing in a large set of photos. You assume people tagged in the same photo are friends (we'll include acquaintances and family as "friends"). How would you heuristically infer potential additional friendships of people who do not appear together in a photo in this photo tag dataset? How would you rank these friendships from most to least likely? Name any specific methods you propose to use.

3 ,. \ 4. [4pts] For a number of stores (you only know the unique store ID) and a range of products (you only know the unique product ID), you observe last year's demand (amount of product each store sold) for a subset of products (not every store sells every product). All stores have different demand profiles but some stores are more similar than others. This year you would like to predict what additional products each store should sell by predicting items that may be in high demand. You can assume that last year's demand will be a good predictor for this year. (a) [2pts] For a given store, how would you predict next year's demand for items that the store did not sell in the previous year? How would you use this predictor to select the top 10 products to add to each store? (I.e., products the store did not sell in the previous year.) (b) [2pts] Now you are not just given last year's data, but the last 10 years of data for all stores. How would this change your prediction model and method? Just provide the high-level ideawhat you will model given this new data and the name of the method you will use for prediction? 5. [2pts] Examine the graduate admissions statistics at right for University X. (Note that the numbers are correct!) In total, men have a 6% greater admission rate than women. Assuming this difference is statistically significant, is it fair Department to claim a gender bias in admission policy? Yes or no? Explain and specify any phenomena you observe in this table that justify your explanation. Men Women Applicants Admitted Applicants Admitted Engineering % % Humanities 101 6% 341 7% Total % %

4 6. [3pts] You have collected a dataset of 1 million tweets, all containing emoticons :). You want to train a binary sentiment analyzer for Twitter that takes a tweet as input and predicts positive sentiment as either true or false (false includes both neutral and negative sentiment- anything that is not positive). The sentiment analyzer you build should work for tweets that do not have emoticons. You do not have the resources to manually label tweets :(. (a) [lpt] How do you automatically process a tweet to heuristically assign a label to each tweet? (b) [2pts] Using the labeled rnreet data, how would you build a sentiment analyzer to predict positive sentiment for new tweets that do not contain emoticons? Clearly name methods you use, a high-level description is sufficient. A key detail is to specify features you will use/omit. 7. [3pts] You are given a corpus of 10 billion tweets (1 tweet= 1 document) such challenge: draw a line that is not a line" and you aim to use MapReduce to count the document frequency (not term frequency) of all tokens in the tweet. In pseudocode or words, describe a mapper and reducer: each tweet is fed to the mapper as a list of all tokens (this list may have repeats if a token occurs more than once). [lpt] flatmap(token_list)-> <K,V>* What list of key value pairs <K,V>* is produced? [lpt] reduce(k,v*)-> V' For key Kand list of values V*, what V' is output? [lpt] Can you use a Combiner for this task? Yes or no. Briefly explain.

5 \ 8. [2pts] Cyberbullying may be defined as a large group of Twitter users who send abusive messages directly to one or more target users (by mentioning them in an abusive tweet). Assume that you have a large Twitter dataset consisting only of tweets that have been verified to contain cyberbullying. Explain how you could automatically process this Twitter data in order to (i) rank possible targets of cyberbullying from most likely to least likely to be bullied as well as (ii) rank possible cyberbullies from most active to least active. Additional answer space ( only use if absolutely necessary - answers should be succinct).

PLEASE HAND IN UNIVERSITY OF TORONTO Faculty of Arts and Science

PLEASE HAND IN UNIVERSITY OF TORONTO Faculty of Arts and Science APRIL 2017 EXAMINATIONS CSC 104 H1S Instructor(s): G. Baumgartner Duration 3 hours PLEASE HAND IN No Aids Allowed Student Number: Last (Family)