1,016 research outputs found
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
Recommender systems widely use implicit feedback such as click data because
of its general availability. Although the presence of clicks signals the users'
preference to some extent, the lack of such clicks does not necessarily
indicate a negative response from the users, as it is possible that the users
were not exposed to the items (positive-unlabeled problem). This leads to a
difficulty in predicting the users' preferences from implicit feedback.
Previous studies addressed the positive-unlabeled problem by uniformly
upweighting the loss for the positive feedback data or estimating the
confidence of each data having relevance information via the EM-algorithm.
However, these methods failed to address the missing-not-at-random problem in
which popular or frequently recommended items are more likely to be clicked
than other items even if a user does not have a considerable interest in them.
To overcome these limitations, we first define an ideal loss function to be
optimized to realize recommendations that maximize the relevance and propose an
unbiased estimator for the ideal loss. Subsequently, we analyze the variance of
the proposed unbiased estimator and further propose a clipped estimator that
includes the unbiased estimator as a special case. We demonstrate that the
clipped estimator is expected to improve the performance of the recommender
system, by considering the bias-variance trade-off. We conduct semi-synthetic
and real-world experiments and demonstrate that the proposed method largely
outperforms the baselines. In particular, the proposed method works better for
rare items that are less frequently observed in the training data. The findings
indicate that the proposed method can better achieve the objective of
recommending items with the highest relevance.Comment: accepted at WSDM'2
Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce
In this paper, we present our work towards comparing on-line and off-line
evaluation metrics in the context of small e-commerce recommender systems.
Recommending on small e-commerce enterprises is rather challenging due to the
lower volume of interactions and low user loyalty, rarely extending beyond a
single session. On the other hand, we usually have to deal with lower volumes
of objects, which are easier to discover by users through various
browsing/searching GUIs.
The main goal of this paper is to determine applicability of off-line
evaluation metrics in learning true usability of recommender systems (evaluated
on-line in A/B testing). In total 800 variants of recommending algorithms were
evaluated off-line w.r.t. 18 metrics covering rating-based, ranking-based,
novelty and diversity evaluation. The off-line results were afterwards compared
with on-line evaluation of 12 selected recommender variants and based on the
results, we tried to learn and utilize an off-line to on-line results
prediction model.
Off-line results shown a great variance in performance w.r.t. different
metrics with the Pareto front covering 68\% of the approaches. Furthermore, we
observed that on-line results are considerably affected by the novelty of
users. On-line metrics correlates positively with ranking-based metrics (AUC,
MRR, nDCG) for novice users, while too high values of diversity and novelty had
a negative impact on the on-line results for them. For users with more visited
items, however, the diversity became more important, while ranking-based
metrics relevance gradually decrease.Comment: Submitted to ACM Hypertext 2020 Conferenc
Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control
Generally speaking, the model training for recommender systems can be based
on two types of data, namely explicit feedback and implicit feedback. Moreover,
because of its general availability, we see wide adoption of implicit feedback
data, such as click signal. There are mainly two challenges for the application
of implicit feedback. First, implicit data just includes positive feedback.
Therefore, we are not sure whether the non-interacted items are really negative
or positive but not displayed to the corresponding user. Moreover, the
relevance of rare items is usually underestimated since much fewer positive
feedback of rare items is collected compared with popular ones. To tackle such
difficulties, both pointwise and pairwise solutions are proposed before for
unbiased relevance learning. As pairwise learning suits well for the ranking
tasks, the previously proposed unbiased pairwise learning algorithm already
achieves state-of-the-art performance. Nonetheless, the existing unbiased
pairwise learning method suffers from high variance. To get satisfactory
performance, non-negative estimator is utilized for practical variance control
but introduces additional bias. In this work, we propose an unbiased pairwise
learning method, named UPL, with much lower variance to learn a truly unbiased
recommender model. Extensive offline experiments on real world datasets and
online A/B testing demonstrate the superior performance of our proposed method.Comment: 5 page
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
In most real-world recommender systems, the observed rating data are subject
to selection bias, and the data are thus missing-not-at-random. Developing a
method to facilitate the learning of a recommender with biased feedback is one
of the most challenging problems, as it is widely known that naive approaches
under selection bias often lead to suboptimal results. A well-established
solution for the problem is using propensity scoring techniques. The propensity
score is the probability of each data being observed, and unbiased performance
estimation is possible by weighting each data by the inverse of its propensity.
However, the performance of the propensity-based unbiased estimation approach
is often affected by choice of the propensity estimation model or the high
variance problem. To overcome these limitations, we propose a model-agnostic
meta-learning method inspired by the asymmetric tri-training framework for
unsupervised domain adaptation. The proposed method utilizes two predictors to
generate data with reliable pseudo-ratings and another predictor to make the
final predictions. In a theoretical analysis, a propensity-independent upper
bound of the true performance metric is derived, and it is demonstrated that
the proposed method can minimize this bound. We conduct comprehensive
experiments using public real-world datasets. The results suggest that the
previous propensity-based methods are largely affected by the choice of
propensity models and the variance problem caused by the inverse propensity
weighting. Moreover, we show that the proposed meta-learning method is robust
to these issues and can facilitate in developing effective recommendations from
biased explicit feedback.Comment: 43rd International ACM SIGIR Conference on Research and Development
in Information Retrieval (SIGIR '20
DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
Recommendation models trained on the user feedback collected from deployed
recommendation systems are commonly biased. User feedback is considerably
affected by the exposure mechanism, as users only provide feedback on the items
exposed to them and passively ignore the unexposed items, thus producing
numerous false negative samples. Inevitably, biases caused by such user
feedback are inherited by new models and amplified via feedback loops.
Moreover, the presence of false negative samples makes negative sampling
difficult and introduces spurious information in the user preference modeling
process of the model. Recent work has investigated the negative impact of
feedback loops and unknown exposure mechanisms on recommendation quality and
user experience, essentially treating them as independent factors and ignoring
their cross-effects. To address these issues, we deeply analyze the data
exposure mechanism from the perspective of data iteration and feedback loops
with the Missing Not At Random (\textbf{MNAR}) assumption, theoretically
demonstrating the existence of an available stabilization factor in the
transformation of the exposure mechanism under the feedback loops. We further
propose Dynamic Personalized Ranking (\textbf{DPR}), an unbiased algorithm that
uses dynamic re-weighting to mitigate the cross-effects of exposure mechanisms
and feedback loops without additional information. Furthermore, we design a
plugin named Universal Anti-False Negative (\textbf{UFN}) to mitigate the
negative impact of the false negative problem. We demonstrate theoretically
that our approach mitigates the negative effects of feedback loops and unknown
exposure mechanisms. Experimental results on real-world datasets demonstrate
that models using DPR can better handle bias accumulation and the universality
of UFN in mainstream loss methods
New accurate, explainable, and unbiased machine learning models for recommendation with implicit feedback.
Recommender systems have become ubiquitous Artificial Intelligence (AI) tools that play an important role in filtering online information in our daily lives. Whether we are shopping, browsing movies, or listening to music online, AI recommender systems are working behind the scene to provide us with curated and personalized content, that has been predicted to be relevant to our interest. The increasing prevalence of recommender systems has challenged researchers to develop powerful algorithms that can deliver recommendations with increasing accuracy. In addition to the predictive accuracy of recommender systems, recent research has also started paying attention to their fairness, in particular with regard to the bias and transparency of their predictions. This dissertation contributes to advancing the state of the art in fairness in AI by proposing new Machine Learning models and algorithms that aim to improve the user\u27s experience when receiving recommendations, with a focus that is positioned at the nexus of three objectives, namely accuracy, transparency, and unbiasedness of the predictions. In our research, we focus on state-of-the-art Collaborative Filtering (CF) recommendation approaches trained on implicit feedback data. More specifically, we address the limitations of two established deep learning approaches in two distinct recommendation settings, namely recommendation with user profiles and sequential recommendation. First, we focus on a state of the art pairwise ranking model, namely Bayesian Personalized Ranking (BPR), which has been found to outperform pointwise models in predictive accuracy in the recommendation with the user profiles setting. Specifically, we address two limitations of BPR: (1) BPR is a black box model that does not explain its outputs, thus limiting the user\u27s trust in the recommendations, and the analyst\u27s ability to scrutinize a model\u27s outputs; and (2) BPR is vulnerable to exposure bias due to the data being Missing Not At Random (MNAR). This exposure bias usually translates into an unfairness against the least popular items because they risk being under-exposed by the recommender system. We propose a novel explainable loss function and a corresponding model called Explainable Bayesian Personalized Ranking (EBPR) that generates recommendations along with item-based explanations. Then, we theoretically quantify the additional exposure bias resulting from the explainability, and use it as a basis to propose an unbiased estimator for the ideal EBPR loss. This being done, we perform an empirical study on three real-world benchmarking datasets that demonstrate the advantages of our proposed models, compared to existing state of the art techniques. Next, we shift our attention to sequential recommendation systems and focus on modeling and mitigating exposure bias in BERT4Rec, which is a state-of-the-art recommendation approach based on bidirectional transformers. The bi-directional representation capacity in BERT4Rec is based on the Cloze task, a.k.a. Masked Language Model, which consists of predicting randomly masked items within the sequence, assuming that the true interacted item is the most relevant one. This results in an exposure bias, where non-interacted items with low exposure propensities are assumed to be irrelevant. Thus far, the most common approach to mitigating exposure bias in recommendation has been Inverse Propensity Scoring (IPS), which consists of down-weighting the interacted predictions in the loss function in proportion to their propensities of exposure, yielding a theoretically unbiased learning. We first argue and prove that IPS does not extend to sequential recommendation because it fails to account for the sequential nature of the problem. We then propose a novel propensity scoring mechanism, that we name Inverse Temporal Propensity Scoring (ITPS), which is used to theoretically debias the Cloze task in sequential recommendation. We also rely on the ITPS framework to propose a bidirectional transformer-based model called ITPS-BERT4Rec. Finally, we empirically demonstrate the debiasing capabilities of our proposed approach and its robustness to the severity of exposure bias. Our proposed explainable approach in recommendation with user profiles, EBPR, showed an increase in ranking accuracy of about 4% and an increase in explainability of about 7% over the baseline BPR model when performing experiments on real-world recommendation datasets. Moreover, experiments on a real-world unbiased dataset demonstrated the importance of coupling explainability and exposure debiasing in capturing the true preferences of the user with a significant improvement of 1% over the baseline unbiased model UBPR. Furthermore, coupling explainability with exposure debiasing was also empirically proven to mitigate popularity bias with an improvement in popularity debiasing metrics of over 10% on three real-world recommendation tasks over the unbiased UBPR model. These results demonstrate the viability of our proposed approaches in recommendation with user profiles and their capacity to improve the user\u27s experience in recommendation by better capturing and modeling their true preferences, improving the explainability of the recommendations, and presenting them with more diverse recommendations that span a larger portion of the item catalog. On the other hand, our proposed approach in sequential recommendation ITPS-BERT4Rec has demonstrated a significant increase of 1% in terms of modeling the true preferences of the user in a semi-synthetic setting over the state-of-the-art sequential recommendation model BERT4Rec while also being unbiased in terms of exposure. Similarly, ITPS-BERT4Rec showed an average increase of 8.7% over BERT4Rec in three real-world recommendation settings. Moreover, empirical experiments demonstrated the robustness of our proposed ITPS-BERT4Rec model to increasing levels of exposure bias and its stability in terms of variance. Furthermore, experiments on popularity debiasing showed a significant advantage of our proposed ITPS-BERT4Rec model for both the short and long term sequences. Finally, ITPS-BERT4Rec showed respective improvements of around 60%, 470%, and 150% over vanilla BERT4Rec in capturing the temporal dependencies between the items within the sequences of interactions for three different evaluation metrics. These results demonstrate the potential of our proposed unbiased estimator to improve the user experience in the context of sequential recommendation by presenting them with more accurate and diverse recommendations that better match their true preferences and the sequential dependencies between the recommended items
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