Personalized Recommendation of PoIs to People with Autism

• URL: http://arxiv.org/abs/2004.12733v1
• Date: Mon, 27 Apr 2020 12:04:58 GMT
• Title: Personalized Recommendation of PoIs to People with Autism
• Authors: Noemi Mauro, Liliana Ardissono and Federica Cena
• Abstract summary: We propose a Top-N recommendation model that combines the user's idiosyncratic aversions with her/his preferences in a personalized way. We tested our model on both ASD and "neurotypical" people.
• Score: 5.052126684056964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced by idiosyncratic sensory aversions which can mine their experience by causing stress and anxiety. Therefore, managing individual preferences is not enough to provide these people with suitable recommendations. In order to address this issue, we propose a Top-N recommendation model that combines the user's idiosyncratic aversions with her/his preferences in a personalized way to suggest the most compatible and likable Points of Interest for her/him. We are interested in finding a user-specific balance of compatibility and interest within a recommendation model that integrates heterogeneous evaluation criteria to appropriately take these aspects into account. We tested our model on both ASD and "neurotypical" people. The evaluation results show that, on both groups, our model outperforms in accuracy and ranking capability the recommender systems based on item compatibility, on user preferences, or which integrate these two aspects by means of a uniform evaluation model.

Related papers

• ComPO: Community Preferences for Language Model Personalization [122.54846260663922]
  ComPO is a method to personalize preference optimization in language models. We collect and release ComPRed, a question answering dataset with community-level preferences from Reddit.
  arXiv  Detail & Related papers  (2024-10-21T14:02:40Z)
• Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
  We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels. We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
  arXiv  Detail & Related papers  (2024-10-09T04:20:15Z)
• A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation [77.42486522565295]
  We propose a novel recommendation approach called LSVCR to jointly conduct personalized video and comment recommendation. Our approach consists of two key components, namely sequential recommendation (SR) model and supplemental large language model (LLM) recommender. In particular, we achieve a significant overall gain of 4.13% in comment watch time.
  arXiv  Detail & Related papers  (2024-03-20T13:14:29Z)
• Separating and Learning Latent Confounders to Enhancing User Preferences Modeling [6.0853798070913845]
  We propose a novel framework, Separating and Learning Latent Confounders For Recommendation (SLFR) SLFR obtains the representation of unmeasured confounders to identify the counterfactual feedback by disentangling user preferences and unmeasured confounders. Experiments in five real-world datasets validate the advantages of our method.
  arXiv  Detail & Related papers  (2023-11-02T08:42:50Z)
• Justification of Recommender Systems Results: A Service-based Approach [4.640835690336653]
  We propose a novel justification approach that uses service models to extract experience data from reviews concerning all the stages of interaction with items. In a user study, we compared our approach with baselines reflecting the state of the art in the justification of recommender systems results. Our models received higher Interface Adequacy and Satisfaction evaluations by users having different levels of Curiosity or low Need for Cognition (NfC) These findings encourage the adoption of service models to justify recommender systems results but suggest the investigation of personalization strategies to suit diverse interaction needs.
  arXiv  Detail & Related papers  (2022-11-07T11:08:19Z)
• Experiments on Generalizability of User-Oriented Fairness in Recommender Systems [2.0932879442844476]
  A fairness-aware recommender system aims to treat different user groups similarly. We propose a user-centered fairness re-ranking framework applied on top of a base ranking model. We evaluate the final recommendations provided by the re-ranking framework from both user- (e.g., NDCG) and item-side (e.g., novelty, item-fairness) metrics.
  arXiv  Detail & Related papers  (2022-05-17T12:36:30Z)
• Joint Multisided Exposure Fairness for Recommendation [76.75990595228666]
  This paper formalizes a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation.
  arXiv  Detail & Related papers  (2022-04-29T19:13:23Z)
• 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)
• Deep Causal Reasoning for Recommendations [47.83224399498504]
  A new trend in recommender system research is to negate the influence of confounders from a causal perspective. We model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. We show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional causal space.
  arXiv  Detail & Related papers  (2022-01-06T15:00:01Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)
• Enabling the Analysis of Personality Aspects in Recommender Systems [0.0]
  Existing recommender systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI) We identify users' personality type implicitly with no burden on users and incorporate it along with users' personal interests and their level of knowledge.
  arXiv  Detail & Related papers  (2020-01-07T23:02:07Z)

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.