Tensor-based Collaborative Filtering With Smooth Ratings Scale

• URL: http://arxiv.org/abs/2205.05070v1
• Date: Tue, 10 May 2022 17:55:25 GMT
• Title: Tensor-based Collaborative Filtering With Smooth Ratings Scale
• Authors: Nikita Marin, Elizaveta Makhneva, Maria Lysyuk, Vladimir Chernyy, Ivan Oseledets, Evgeny Frolov
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item. Even if they had experience with the same items this systematic discrepancy in their evaluation style will lead to the systematic errors in the ability of recommender system to effectively extract right patterns from data. To mitigate this problem we introduce the ratings' similarity matrix which represents the dependency between different values of ratings on the population level. Hence, if on average the correlations between ratings exist, it is possible to improve the quality of proposed recommendations by off-setting the effect of either shifted down or shifted up users' rates.

Related papers

• Analytical and Empirical Study of Herding Effects in Recommendation Systems [72.6693986712978]
  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.
  arXiv  Detail & Related papers  (2024-08-20T14:29:23Z)
• Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs [57.16442740983528]
  In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback. The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied. We focus on how the evaluation of task-oriented dialogue systems ( TDSs) is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated.
  arXiv  Detail & Related papers  (2024-04-19T16:45:50Z)
• Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems [1.4279471205248533]
  We find that users with different rating dispositions may use the recommender system differently. We find that such partitioning improves computational efficiency but also improves top-k performance and predictive accuracy.
  arXiv  Detail & Related papers  (2023-06-20T04:16:53Z)
• 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)
• Causal Disentanglement with Network Information for Debiased Recommendations [34.698181166037564]
  Recent research proposes to debias by modeling a recommender system from a causal perspective. The critical challenge in this setting is accounting for the hidden confounders. We propose to leverage network information (i.e., user-social and user-item networks) to better approximate hidden confounders.
  arXiv  Detail & Related papers  (2022-04-14T20:55:11Z)
• Unbiased Pairwise Learning to Rank in Recommender Systems [4.058828240864671]
  Unbiased learning to rank algorithms are appealing candidates and have already been applied in many applications with single categorical labels. We propose a novel unbiased LTR algorithm to tackle the challenges, which innovatively models position bias in the pairwise fashion. Experiment results on public benchmark datasets and internal live traffic show the superior results of the proposed method for both categorical and continuous labels.
  arXiv  Detail & Related papers  (2021-11-25T06:04:59Z)
• Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons [85.5955376526419]
  In rank aggregation problems, users exhibit various accuracy levels when comparing pairs of items. We propose an elimination-based active sampling strategy, which estimates the ranking of items via noisy pairwise comparisons. We prove that our algorithm can return the true ranking of items with high probability.
  arXiv  Detail & Related papers  (2021-10-08T13:51:55Z)
• Correcting the User Feedback-Loop Bias for Recommendation Systems [34.44834423714441]
  We propose a systematic and dynamic way to correct user feedback-loop bias in recommendation systems. Our method includes a deep-learning component to learn each user's dynamic rating history embedding. We empirically validated the existence of such user feedback-loop bias in real world recommendation systems.
  arXiv  Detail & Related papers  (2021-09-13T15:02:55Z)
• Modurec: Recommender Systems with Feature and Time Modulation [50.51144496609274]
  We propose Modurec: an autoencoder-based method that combines all available information using the feature-wise modulation mechanism. We show on Movielens datasets that these modifications produce state-of-the-art results in most evaluated settings.
  arXiv  Detail & Related papers  (2020-10-13T09:18:33Z)
• A Robust Reputation-based Group Ranking System and its Resistance to Bribery [8.300507994596416]
  We propose a new reputation-based ranking system, utilizing multipartite ratingworks. We study its resistance to bribery and how to design optimal bribing strategies.
  arXiv  Detail & Related papers  (2020-04-13T22:28:29Z)
• SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback [50.13745601531148]
  We propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons. We also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to $sqrtM/N$.
  arXiv  Detail & Related papers  (2020-02-23T06:40:48Z)

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.