Analytical and Empirical Study of Herding Effects in Recommendation Systems
- URL: http://arxiv.org/abs/2408.10895v1
- Date: Tue, 20 Aug 2024 14:29:23 GMT
- Title: Analytical and Empirical Study of Herding Effects in Recommendation Systems
- Authors: Hong Xie, Mingze Zhong, Defu Lian, Zhen Wang, Enhong Chen,
- Abstract summary: We study how to manage product ratings via rating aggregation rules and shortlisted representative reviews.
We show that proper recency aware rating aggregation rules can improve the speed of convergence in Amazon and TripAdvisor.
- Score: 72.6693986712978
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Online rating systems are often used in numerous web or mobile applications, e.g., Amazon and TripAdvisor, to assess the ground-truth quality of products. Due to herding effects, the aggregation of historical ratings (or historical collective opinion) can significantly influence subsequent ratings, leading to misleading and erroneous assessments. We study how to manage product ratings via rating aggregation rules and shortlisted representative reviews, for the purpose of correcting the assessment error. We first develop a mathematical model to characterize important factors of herding effects in product ratings. We then identify sufficient conditions (via the stochastic approximation theory), under which the historical collective opinion converges to the ground-truth collective opinion of the whole user population. These conditions identify a class of rating aggregation rules and review selection mechanisms that can reveal the ground-truth product quality. We also quantify the speed of convergence (via the martingale theory), which reflects the efficiency of rating aggregation rules and review selection mechanisms. We prove that the herding effects slow down the speed of convergence while an accurate review selection mechanism can speed it up. We also study the speed of convergence numerically and reveal trade-offs in selecting rating aggregation rules and review selection mechanisms. To show the utility of our framework, we design a maximum likelihood algorithm to infer model parameters from ratings, and conduct experiments on rating datasets from Amazon and TripAdvisor. We show that proper recency aware rating aggregation rules can improve the speed of convergence in Amazon and TripAdvisor by 41% and 62% respectively.
Related papers
- Fighting Sampling Bias: A Framework for Training and Evaluating Credit Scoring Models [2.918530881730374]
This paper addresses the adverse effect of sampling bias on model training and evaluation.
We propose bias-aware self-learning and a reject inference framework for scorecard evaluation.
Our results suggest a profit improvement of about eight percent, when using Bayesian evaluation to decide on acceptance rates.
arXiv Detail & Related papers (2024-07-17T20:59:54Z) - Estimating Treatment Effects under Recommender Interference: A Structured Neural Networks Approach [13.208141830901845]
We show that the standard difference-in-means estimator can lead to biased estimates due to recommender interference.
We propose a "recommender choice model" that describes which item gets exposed from a pool containing both treated and control items.
We show that the proposed estimator yields results comparable to the benchmark, whereas the standard difference-in-means estimator can exhibit significant bias and even produce reversed signs.
arXiv Detail & Related papers (2024-06-20T14:53:26Z) - On Faithfulness and Coherence of Language Explanations for
Recommendation Systems [8.143715142450876]
This work probes state-of-the-art models and their review generation component.
We show that the generated explanations are brittle and need further evaluation before being taken as literal rationales for the estimated ratings.
arXiv Detail & Related papers (2022-09-12T17:00:31Z) - Tensor-based Collaborative Filtering With Smooth Ratings Scale [0.0]
We introduce the ratings' similarity matrix which represents the dependency between different values of ratings on the population level.
It is possible to improve the quality of proposed recommendations by off-setting the effect of either shifted down or shifted up users' rates.
arXiv Detail & Related papers (2022-05-10T17:55:25Z) - Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
We develop a new learning paradigm named Cross Pairwise Ranking (CPR)
CPR achieves unbiased recommendation without knowing the exposure mechanism.
We prove in theory that this way offsets the influence of user/item propensity on the learning.
arXiv Detail & Related papers (2022-04-26T09:20:27Z) - Spatio-Temporal Graph Representation Learning for Fraudster Group
Detection [50.779498955162644]
Companies may hire fraudster groups to write fake reviews to either demote competitors or promote their own businesses.
To detect such groups, a common model is to represent fraudster groups' static networks.
We propose to first capitalize on the effectiveness of the HIN-RNN in both reviewers' representation learning.
arXiv Detail & Related papers (2022-01-07T08:01:38Z) - Deconfounding Scores: Feature Representations for Causal Effect
Estimation with Weak Overlap [140.98628848491146]
We introduce deconfounding scores, which induce better overlap without biasing the target of estimation.
We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data.
In particular, we show that this technique could be an attractive alternative to standard regularizations.
arXiv Detail & Related papers (2021-04-12T18:50:11Z) - ScoreGAN: A Fraud Review Detector based on Multi Task Learning of
Regulated GAN with Data Augmentation [50.779498955162644]
We propose ScoreGAN for fraud review detection that makes use of both review text and review rating scores in the generation and detection process.
Results show that the proposed framework outperformed the existing state-of-the-art framework, namely FakeGAN, in terms of AP by 7%, and 5% on the Yelp and TripAdvisor datasets.
arXiv Detail & Related papers (2020-06-11T16:15:06Z) - A Unified Dual-view Model for Review Summarization and Sentiment
Classification with Inconsistency Loss [51.448615489097236]
Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms.
We propose a novel dual-view model that jointly improves the performance of these two tasks.
Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.
arXiv Detail & Related papers (2020-06-02T13:34:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.