Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a…

Option A is incorrect because creating a collaborative filtering system that recommends articles to a

user based on the user’s past behavior is not the best approach to suggest articles that are similar to

the articles they are currently reading. Collaborative filtering is a method of recommendation that

uses the ratings or p

of other users to predict the preferences of a target user1. However,

this method does not consider the content or features of the articles, and may not be able to find

articles that are similar in terms of topic, style, or sentiment.

Option B is correct because encoding all articles into vectors using word2vec, and building a model

that returns articles based on vector similarity is a suitable approach to suggest articles that are

similar to the articles they are currently reading. Word2vec is a technique that learns low-

dimensional and dense representations of words from a large corpus of text, such that words that are

semantically similar have similar vectors2. By applying word2vec to the articles, we can obtain vector

representations of the articles that capture their meaning and usage. Then, we can use a similarity

measure, such as cosine similarity, to find articles that have similar vectors to the current article3.

Option C is incorrect because building a logistic regression model for each user that predicts whether

an article should be recommended to a user is not a feasible approach to suggest articles that are

similar to the articles they are currently reading. Logistic regression is a supervised learning method

that models the probability of a binary outcome (such as recommend or not) based on some input

features (such as user profile or article content)4. However, this method requires a large amount of

labeled data for each user, which may not be available or scalable. Moreover, this method does not

directly measure the similarity between articles, but rather the likelihood of a user’s preference.

Option D is incorrect because manually labeling a few hundred articles, and then training an SVM

classifier based on the manually classified articles that categorizes additional articles into their

respective categories is not an effective approach to suggest articles that are similar to the articles

they are currently reading. SVM (support vector machine) is a supervised learning method that finds

a hyperplane that separates the data into different classes (such as news categories) with the

maximum margin5. However, this method also requires a large amount of labeled data, which may

be costly and time-consuming to obtain. Moreover, this method does not account for the fine-

grained similarity between articles within the same category, or the cross-category similarity

between articles from different categories.

Reference:

Collaborative filtering

Word2vec

Cosine similarity

Logistic regression

SVM