Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback

• URL: http://arxiv.org/abs/2203.12598v1
• Date: Fri, 18 Mar 2022 18:08:57 GMT
• Title: Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
• Authors: Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis
• Abstract summary: This paper studies the item-to-item recommendation problem in recommender systems from a new perspective of metric learning via implicit feedback. We develop and investigate a personalizable deep metric model that captures both the internal contents of items and how they were interacted with by users.
• Score: 24.37151414523712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies the item-to-item recommendation problem in recommender systems from a new perspective of metric learning via implicit feedback. We develop and investigate a personalizable deep metric model that captures both the internal contents of items and how they were interacted with by users. There are two key challenges in learning such model. First, there is no explicit similarity annotation, which deviates from the assumption of most metric learning methods. Second, these approaches ignore the fact that items are often represented by multiple sources of meta data and different users use different combinations of these sources to form their own notion of similarity. To address these challenges, we develop a new metric representation embedded as kernel parameters of a probabilistic model. This helps express the correlation between items that a user has interacted with, which can be used to predict user interaction with new items. Our approach hinges on the intuition that similar items induce similar interactions from the same user, thus fitting a metric-parameterized model to predict an implicit feedback signal could indirectly guide it towards finding the most suitable metric for each user. To this end, we also analyze how and when the proposed method is effective from a theoretical lens. Its empirical effectiveness is also demonstrated on several real-world datasets.

Related papers

• Leveraging a Cognitive Model to Measure Subjective Similarity of Human and GPT-4 Written Content [26.409490082213445]
  Cosine similarity between two documents can be computed using token embeddings formed by Large Language Models (LLMs) such as GPT-4. This similarity metric is beneficial in that it takes into account individual biases and constraints in a manner that is grounded in the cognitive mechanisms of decision making. This dataset is used to demonstrate the benefits of leveraging a cognitive model to measure the subjective similarity of human participants in an educational setting.
  arXiv  Detail & Related papers  (2024-08-30T21:54:13Z)
• Semantic-Enhanced Relational Metric Learning for Recommender Systems [27.330164862413184]
  Recently, metric learning methods have been received great attention in recommendation community, which is inspired by the translation mechanism in knowledge graph. We propose a joint Semantic-Enhanced Metric Learning framework to tackle the problem in recommender systems. Specifically the semantic signal is first extracted from the target reviews containing abundant features and personalized user preferences. A novel regression model is then designed via leveraging the extracted semantic signal to improve the discriminative ability of original relation-based training process.
  arXiv  Detail & Related papers  (2024-06-07T11:54:50Z)
• 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)
• Topology-aware Debiased Self-supervised Graph Learning for Recommendation [6.893289671937124]
  We propose Topology-aware De Self-supervised Graph Learning ( TDSGL) for recommendation. TDSGL constructs contrastive pairs according to the semantic similarity between users (items) Our results show that the proposed model outperforms the state-of-the-art models significantly on three public datasets.
  arXiv  Detail & Related papers  (2023-10-24T14:16:19Z)
• The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm [154.47590401735323]
  Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems. This paper focuses on a challenging scenario where a user has multiple categories of interests. We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
  arXiv  Detail & Related papers  (2022-09-30T08:02:18Z)
• One for All: Simultaneous Metric and Preference Learning over Multiple Users [17.083305162005136]
  We study the simultaneous preference and metric learning from a crowd of respondents. Our model jointly learns a distance metric that characterizes the crowd's general measure of item similarities. We demonstrate the performance of our model on both simulated data and on a dataset of color preference judgements.
  arXiv  Detail & Related papers  (2022-07-07T22:47:13Z)
• Adaptive Hierarchical Similarity Metric Learning with Noisy Labels [138.41576366096137]
  We propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, textiti.e., class-wise divergence and sample-wise consistency. Our method achieves state-of-the-art performance compared with current deep metric learning approaches.
  arXiv  Detail & Related papers  (2021-10-29T02:12:18Z)
• Combining Feature and Instance Attribution to Detect Artifacts [62.63504976810927]
  We propose methods to facilitate identification of training data artifacts. We show that this proposed training-feature attribution approach can be used to uncover artifacts in training data. We execute a small user study to evaluate whether these methods are useful to NLP researchers in practice.
  arXiv  Detail & Related papers  (2021-07-01T09:26:13Z)
• On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering [10.64460581091531]
  We present an approach to use causal inference to learn user-attribute affinities through temporal contexts. We formulate this objective as a Probabilistic Machine Learning problem and apply a variational inference based method to estimate the model parameters.
  arXiv  Detail & Related papers  (2020-12-02T22:39:58Z)
• Interpretable Multi-dataset Evaluation for Named Entity Recognition [110.64368106131062]
  We present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area.
  arXiv  Detail & Related papers  (2020-11-13T10:53:27Z)
• 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)

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.