36,593 research outputs found
Exploring Author Gender in Book Rating and Recommendation
Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as gender or ethnic discrimination in publishing. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution
Exploration of User Groups in VEXUS
We introduce VEXUS, an interactive visualization framework for exploring user
data to fulfill tasks such as finding a set of experts, forming discussion
groups and analyzing collective behaviors. User data is characterized by a
combination of demographics like age and occupation, and actions such as rating
a movie, writing a paper, following a medical treatment or buying groceries.
The ubiquity of user data requires tools that help explorers, be they
specialists or novice users, acquire new insights. VEXUS lets explorers
interact with user data via visual primitives and builds an exploration profile
to recommend the next exploration steps. VEXUS combines state-of-the-art
visualization techniques with appropriate indexing of user data to provide fast
and relevant exploration
CausaLM: Causal Model Explanation Through Counterfactual Language Models
Understanding predictions made by deep neural networks is notoriously
difficult, but also crucial to their dissemination. As all ML-based methods,
they are as good as their training data, and can also capture unwanted biases.
While there are tools that can help understand whether such biases exist, they
do not distinguish between correlation and causation, and might be ill-suited
for text-based models and for reasoning about high level language concepts. A
key problem of estimating the causal effect of a concept of interest on a given
model is that this estimation requires the generation of counterfactual
examples, which is challenging with existing generation technology. To bridge
that gap, we propose CausaLM, a framework for producing causal model
explanations using counterfactual language representation models. Our approach
is based on fine-tuning of deep contextualized embedding models with auxiliary
adversarial tasks derived from the causal graph of the problem. Concretely, we
show that by carefully choosing auxiliary adversarial pre-training tasks,
language representation models such as BERT can effectively learn a
counterfactual representation for a given concept of interest, and be used to
estimate its true causal effect on model performance. A byproduct of our method
is a language representation model that is unaffected by the tested concept,
which can be useful in mitigating unwanted bias ingrained in the data.Comment: Our code and data are available at:
https://amirfeder.github.io/CausaLM/ Under review for the Computational
Linguistics journa
On the motivating impact of price and online recommendations at the point of online purchase
This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2011 ElsevierDo online recommendations have the same motivating impact as price at the point of online purchase? The results (n = 268) of an conjoint study show that: (1) when the price is low or high relatively to market price, it has the strongest impact (positive and negative) on the likelihood of an online purchase of an mp3 player, (2) when the price is average to market price, online recommendation and price are equal in their impact at the point of online purchase, and, (3) the relative impact from price increases when online shopping frequencies increases. The implications these results give are that online retailers should be aware that online recommendations are not as influential as a good offer when consumers purchase electronics online. However, other customer recommendations have a stronger impact on novice online shoppers than towards those consumers that shop more frequently online
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