6,542 research outputs found
Co-Following on Twitter
We present an in-depth study of co-following on Twitter based on the
observation that two Twitter users whose followers have similar friends are
also similar, even though they might not share any direct links or a single
mutual follower. We show how this observation contributes to (i) a better
understanding of language-agnostic user classification on Twitter, (ii)
eliciting opportunities for Computational Social Science, and (iii) improving
online marketing by identifying cross-selling opportunities.
We start with a machine learning problem of predicting a user's preference
among two alternative choices of Twitter friends. We show that co-following
information provides strong signals for diverse classification tasks and that
these signals persist even when (i) the most discriminative features are
removed and (ii) only relatively "sparse" users with fewer than 152 but more
than 43 Twitter friends are considered.
Going beyond mere classification performance optimization, we present
applications of our methodology to Computational Social Science. Here we
confirm stereotypes such as that the country singer Kenny Chesney
(@kennychesney) is more popular among @GOP followers, whereas Lady Gaga
(@ladygaga) enjoys more support from @TheDemocrats followers.
In the domain of marketing we give evidence that celebrity endorsement is
reflected in co-following and we demonstrate how our methodology can be used to
reveal the audience similarities between Apple and Puma and, less obviously,
between Nike and Coca-Cola. Concerning a user's popularity we find a
statistically significant connection between having a more "average"
followership and having more followers than direct rivals. Interestingly, a
\emph{larger} audience also seems to be linked to a \emph{less diverse}
audience in terms of their co-following.Comment: full version of a short paper at Hypertext 201
Recommender System Using Collaborative Filtering Algorithm
With the vast amount of data that the world has nowadays, institutions are looking for more and more accurate ways of using this data. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item.
Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. However, to reach this goal, systems need to parse a lot of data and collect information, sometimes from different resources, and predict how the user will like the product or item. The computation power needed is considerable. Also, companies try to avoid flooding customer mailboxes with hundreds of products each morning, thus they are looking for one email or text that will make the customer look and act.
The motivation to do the project comes from my eagerness to learn website design and get a deep understanding of recommender systems. Applying machine learning dynamically is one of the goals that I set for myself and I wanted to go beyond that and verify my result. Thus, I had to use a large dataset to test the algorithm and compare each technique in terms of error rate. My experience with applying collaborative filtering helps me to understand that finding a solution is not enough, but to strive for a fast and ultimate one. In my case, testing my algorithm in a large data set required me to refine the coding strategy of the algorithm many times to speed the process.
In this project, I have designed a website that uses different techniques for recommendations. User-based, Item-based, and Model-based approaches of collaborative filtering are what I have used. Every technique has its way of predicting the user rating for a new item based on existing users’ data. To evaluate each method, I used Movie Lens, an external data set of users, items, and ratings, and calculated the error rate using Mean Absolute Error Rate (MAE) and Root Mean Squared Error (RMSE). Finally, each method has its strengths and weaknesses that relate to the domain in which I am applying these methods
Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design
It is well known that collaborative filtering (CF) based recommender systems
provide better modeling of users and items associated with considerable rating
history. The lack of historical ratings results in the user and the item
cold-start problems. The latter is the main focus of this work. Most of the
current literature addresses this problem by integrating content-based
recommendation techniques to model the new item. However, in many cases such
content is not available, and the question arises is whether this problem can
be mitigated using CF techniques only. We formalize this problem as an
optimization problem: given a new item, a pool of available users, and a budget
constraint, select which users to assign with the task of rating the new item
in order to minimize the prediction error of our model. We show that the
objective function is monotone-supermodular, and propose efficient optimal
design based algorithms that attain an approximation to its optimum. Our
findings are verified by an empirical study using the Netflix dataset, where
the proposed algorithms outperform several baselines for the problem at hand.Comment: 11 pages, 2 figure
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