Multi-faceted Trust-based Collaborative Filtering

• URL: http://arxiv.org/abs/2003.11445v1
• Date: Wed, 25 Mar 2020 15:27:06 GMT
• Title: Multi-faceted Trust-based Collaborative Filtering
• Authors: Noemi Mauro, Liliana Ardissono and Zhongli Filippo Hu
• Abstract summary: We propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders.
• Score: 4.640835690336653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality. We are interested in understanding if and when these additional types of feedback improve Top-N recommendation. For this purpose, we propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We aim at identifying general classes of data in order to make our model applicable to different case studies. Then, we test the model by applying it to a variant of User-to-User Collaborative filtering (U2UCF) which supports the fusion of rating similarity, local trust derived from social relations, and multi-faceted reputation for rating prediction. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback, including user profile endorsements. The LibraryThing dataset offers fewer types of feedback but it provides more selective friend relations aimed at content sharing. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders that only use rating similarity and social relations. Differently, in the LibraryThing dataset, the combination of social relations and rating similarity achieves the best results. The lesson we learn is that multi-faceted trust can be a valuable type of information for recommendation. However, before using it in an application domain, an analysis of the type and amount of available trust evidence has to be done to assess its real impact on recommendation performance.

Related papers

• Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities [0.9749638953163389]
  This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities. This paper provides the proposed neural architecture; it also tests that the quality of its recommendation results is as good as the state of art baselines.
  arXiv  Detail & Related papers  (2024-10-22T09:17:05Z)
• A First Principles Approach to Trust-Based Recommendation Systems [4.833815605196965]
  We show that item-rating information is more influential than other information types in a collaborative filtering approach. The trust graph-based approaches were found to be more robust to network adversarial attacks due to hard-to-manipulate trust structures.
  arXiv  Detail & Related papers  (2024-06-17T05:23:00Z)
• Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking [56.80065604034095]
  We introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario.
  arXiv  Detail & Related papers  (2022-10-19T16:19:37Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• Recommendation Systems with Distribution-Free Reliability Guarantees [83.80644194980042]
  We show how to return a set of items rigorously guaranteed to contain mostly good items. Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate. We evaluate our methods on the Yahoo! Learning to Rank and MSMarco datasets.
  arXiv  Detail & Related papers  (2022-07-04T17:49:25Z)
• 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)
• Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management [61.88858330222619]
  We present an approach for predicting trust links between peers in social media. We propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset.
  arXiv  Detail & Related papers  (2021-11-11T19:40:51Z)
• Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries [59.27273928454995]
  Current pre-trained models applied to summarization are prone to factual inconsistencies which misrepresent the source text or introduce extraneous information. We create a crowdsourcing evaluation framework for factual consistency using the rating-based Likert scale and ranking-based Best-Worst Scaling protocols. We find that ranking-based protocols offer a more reliable measure of summary quality across datasets, while the reliability of Likert ratings depends on the target dataset and the evaluation design.
  arXiv  Detail & Related papers  (2021-09-19T19:05:00Z)
• Dual Side Deep Context-aware Modulation for Social Recommendation [50.59008227281762]
  We propose a novel graph neural network to model the social relation and collaborative relation. On top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.
  arXiv  Detail & Related papers  (2021-03-16T11:08:30Z)
• Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks [0.4061135251278187]
  Trust can be defined as a measure to determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected.
  arXiv  Detail & Related papers  (2020-03-21T01:00:02Z)

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.