2,252 research outputs found
Rate of forgetting is independent from initial degree of learning across different age groups
It is well established that the more we learn, the more we remember. It is also known that our ability to acquire new information changes with age. An important remaining issue for debate is whether the rate of forgetting depends on initial degree of learning. In two experiments, following the procedure used by Slamecka and McElree (Exp 3), we investigated the relationship between initial degree of learning and rate of forgetting in both younger and older adults. A set of 36 (Exp 1) and a set of 30 (Exp 2) sentences was presented four times. Forgetting was measured via cued recall at three retention intervals (30 s, 1 hr, and 24 hr). A different third of the original sentences was tested at each delay. The results of both experiments showed that initial acquisition is influenced by age. However, the rate of forgetting proved to be independent from initial degree of learning. The conclusion is that rates of forgetting are independent from initial degree of learning
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Existing benchmark datasets for recommender systems (RS) either are created
at a small scale or involve very limited forms of user feedback. RS models
evaluated on such datasets often lack practical values for large-scale
real-world applications. In this paper, we describe Tenrec, a novel and
publicly available data collection for RS that records various user feedback
from four different recommendation scenarios. To be specific, Tenrec has the
following five characteristics: (1) it is large-scale, containing around 5
million users and 140 million interactions; (2) it has not only positive user
feedback, but also true negative feedback (vs. one-class recommendation); (3)
it contains overlapped users and items across four different scenarios; (4) it
contains various types of user positive feedback, in forms of clicks, likes,
shares, and follows, etc; (5) it contains additional features beyond the user
IDs and item IDs. We verify Tenrec on ten diverse recommendation tasks by
running several classical baseline models per task. Tenrec has the potential to
become a useful benchmark dataset for a majority of popular recommendation
tasks
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
With the growing privacy concerns in recommender systems, recommendation
unlearning, i.e., forgetting the impact of specific learned targets, is getting
increasing attention. Existing studies predominantly use training data, i.e.,
model inputs, as the unlearning target. However, we find that attackers can
extract private information, i.e., gender, race, and age, from a trained model
even if it has not been explicitly encountered during training. We name this
unseen information as attribute and treat it as the unlearning target. To
protect the sensitive attribute of users, Attribute Unlearning (AU) aims to
degrade attacking performance and make target attributes indistinguishable. In
this paper, we focus on a strict but practical setting of AU, namely
Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be
performed after the training of the recommendation model is completed. To
address the PoT-AU problem in recommender systems, we design a two-component
loss function that consists of i) distinguishability loss: making attribute
labels indistinguishable from attackers, and ii) regularization loss:
preventing drastic changes in the model that result in a negative impact on
recommendation performance. Specifically, we investigate two types of
distinguishability measurements, i.e., user-to-user and
distribution-to-distribution. We use the stochastic gradient descent algorithm
to optimize our proposed loss. Extensive experiments on three real-world
datasets demonstrate the effectiveness of our proposed methods
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