SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation

• URL: http://arxiv.org/abs/2209.04154v1
• Date: Fri, 9 Sep 2022 07:24:55 GMT
• Title: SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation
• Authors: Taejun Lim, Siqu Long, Josiah Poon, Soyeon Caren Han
• Abstract summary: Collaborative filtering problems are commonly solved based on matrix completion techniques. This paper proposes a novel position-enhanced user/item representation training model for recommendation, SUPER-Rec.
• Score: 5.5362209010481855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative filtering problems are commonly solved based on matrix completion techniques which recover the missing values of user-item interaction matrices. In a matrix, the rating position specifically represents the user given and the item rated. Previous matrix completion techniques tend to neglect the position of each element (user, item and ratings) in the matrix but mainly focus on semantic similarity between users and items to predict the missing value in a matrix. This paper proposes a novel position-enhanced user/item representation training model for recommendation, SUPER-Rec. We first capture the rating position in the matrix using the relative positional rating encoding and store the position-enhanced rating information and its user-item relationship to the fixed dimension of embedding that is not affected by the matrix size. Then, we apply the trained position-enhanced user and item representations to the simplest traditional machine learning models to highlight the pure novelty of our representation learning model. We contribute the first formal introduction and quantitative analysis of position-enhanced item representation in the recommendation domain and produce a principled discussion about our SUPER-Rec to the outperformed performance of typical collaborative filtering recommendation tasks with both explicit and implicit feedback.

Related papers

• Robust Matrix Completion for Discrete Rating-Scale Data: Coping with Fake Profiles in Recommender Systems [1.8638865257327277]
  Matrix completion is used to predict a user's preferences for items they have not yet rated.<n>We propose a robust discrete matrix completion (RDMC) method to handle the discrete nature of sparse rating-scale data.<n>Our work offers a statistically-sound blueprint for future studies on how to evaluate matrix completion methods for recommender systems under realistic scenarios.
  arXiv  Detail & Related papers  (2024-12-30T08:49:41Z)
• Collaborative filtering based on nonnegative/binary matrix factorization [0.0]
  We propose a modified NBMF algorithm that can be applied to collaborative filtering where data are sparse. In the modified method, unrated elements in a rating matrix are masked, which improves the collaborative filtering performance.
  arXiv  Detail & Related papers  (2024-10-14T11:10:15Z)
• Matryoshka Representation Learning for Recommendation [12.065963915376866]
  We introduce a novel matryoshka representation learning method for recommendation (MRL4Rec) We restructure user and item vectors into matryoshka representations with incrementally dimensional and overlapping vector spaces to explicitly represent user preferences and item features at different hierarchical levels. Experiments demonstrate that MRL4Rec can consistently and substantially outperform a number of state-of-the-art competitors on several real-life datasets.
  arXiv  Detail & Related papers  (2024-06-11T16:34:21Z)
• MMGRec: Multimodal Generative Recommendation with Transformer Model [81.61896141495144]
  MMGRec aims to introduce a generative paradigm into multimodal recommendation. We first devise a hierarchical quantization method Graph CF-RQVAE to assign Rec-ID for each item from its multimodal information. We then train a Transformer-based recommender to generate the Rec-IDs of user-preferred items based on historical interaction sequences.
  arXiv  Detail & Related papers  (2024-04-25T12:11:27Z)
• AutoSeqRec: Autoencoder for Efficient Sequential Recommendation [39.65252473941165]
  This paper presents AutoSeqRec, an incremental recommendation model specifically designed for sequential recommendation tasks. AutoSeqRec is based on autoencoders and consists of an encoder and three decoders within the autoencoder architecture. Comprehensive evaluations demonstrate that AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing its robustness and efficiency.
  arXiv  Detail & Related papers  (2023-08-14T01:23:37Z)
• Graph Collaborative Signals Denoising and Augmentation for Recommendation [75.25320844036574]
  We propose a new graph adjacency matrix that incorporates user-user and item-item correlations. We show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions.
  arXiv  Detail & Related papers  (2023-04-06T19:43:37Z)
• Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation [133.8758914874593]
  Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences.
  arXiv  Detail & Related papers  (2022-04-02T03:23:46Z)
• Learning to Recommend Using Non-Uniform Data [7.005458308454873]
  Learning user preferences for products based on past purchases or reviews is at the cornerstone of modern recommendation engines. Some users are more likely to purchase products or review them, and some products are more likely to be purchased or reviewed by the users. This non-uniform pattern degrades the power of many existing recommendation algorithms.
  arXiv  Detail & Related papers  (2021-10-21T16:17:40Z)
• The Stereotyping Problem in Collaboratively Filtered Recommender Systems [77.56225819389773]
  We show that matrix factorization-based collaborative filtering algorithms induce a kind of stereotyping. If preferences for a textitset of items are anti-correlated in the general user population, then those items may not be recommended together to a user. We propose an alternative modelling fix, which is designed to capture the diverse multiple interests of each user.
  arXiv  Detail & Related papers  (2021-06-23T18:37:47Z)
• Adversarially-Trained Nonnegative Matrix Factorization [77.34726150561087]
  We consider an adversarially-trained version of the nonnegative matrix factorization. In our formulation, an attacker adds an arbitrary matrix of bounded norm to the given data matrix. We design efficient algorithms inspired by adversarial training to optimize for dictionary and coefficient matrices.
  arXiv  Detail & Related papers  (2021-04-10T13:13:17Z)
• Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach [115.76667128325361]
  Recommendation models can effectively estimate underlying user interests and predict one's future behaviors. We propose an inductive collaborative filtering framework that contains two representation models. Our model achieves promising results for recommendation on few-shot users with limited training ratings and new unseen users.
  arXiv  Detail & Related papers  (2020-07-09T14:31:25Z)
• Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering [25.569930256022925]
  We propose a VAE-based Bayesian MF framework to approximate the user-item joint distribution. The framework is iterative with cross feedback of user and item embeddings into each other's encoders. It reconstructs the data via the matrix factorization over the currently re-sampled user and item embeddings.
  arXiv  Detail & Related papers  (2020-02-21T06:05:06Z)

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.