Photos Are All You Need for Reciprocal Recommendation in Online Dating
- URL: http://arxiv.org/abs/2108.11714v1
- Date: Thu, 26 Aug 2021 11:38:23 GMT
- Title: Photos Are All You Need for Reciprocal Recommendation in Online Dating
- Authors: James Neve and Ryan McConville
- Abstract summary: We present a novel method of interpreting user image preference history and using this to make recommendations.
We train a recurrent neural network to learn a user's preferences and make predictions of reciprocal preference relations.
Our system significantly outperforms on the state of the art in both content-based and collaborative filtering systems.
- Score: 1.90365714903665
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recommender Systems are algorithms that predict a user's preference for an
item. Reciprocal Recommenders are a subset of recommender systems, where the
items in question are people, and the objective is therefore to predict a
bidirectional preference relation. They are used in settings such as online
dating services and social networks. In particular, images provided by users
are a crucial part of user preference, and one that is not exploited much in
the literature. We present a novel method of interpreting user image preference
history and using this to make recommendations. We train a recurrent neural
network to learn a user's preferences and make predictions of reciprocal
preference relations that can be used to make recommendations that satisfy both
users. We show that our proposed system achieves an F1 score of 0.87 when using
only photographs to produce reciprocal recommendations on a large real world
online dating dataset. Our system significantly outperforms on the state of the
art in both content-based and collaborative filtering systems.
Related papers
- Dissertation: On the Theoretical Foundation of Model Comparison and Evaluation for Recommender System [4.76281731053599]
Recommender systems utilize users' historical data to infer customer interests and provide personalized recommendations.
Collaborative filtering is one family of recommendation algorithms that uses ratings from multiple users to predict missing ratings.
Recommender systems can be more complex and incorporate auxiliary data such as content-based attributes, user interactions, and contextual information.
arXiv Detail & Related papers (2024-11-04T06:31:52Z) - How to Surprisingly Consider Recommendations? A Knowledge-Graph-based Approach Relying on Complex Network Metrics [0.2537383030441368]
We propose a Knowledge Graph based recommender system by encoding user interactions on item catalogs.
Our study explores whether network-level metrics on KGs can influence the degree of surprise in recommendations.
We experimentally evaluate our approach on two datasets of LastFM listening histories and synthetic Netflix viewing profiles.
arXiv Detail & Related papers (2024-05-14T09:38:44Z) - The Fault in Our Recommendations: On the Perils of Optimizing the Measurable [2.6217304977339473]
We show that optimizing for engagement can lead to significant utility losses.
We propose a utility-aware policy that initially recommends a mix of popular and niche content.
arXiv Detail & Related papers (2024-05-07T02:12:17Z) - 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) - Editable User Profiles for Controllable Text Recommendation [66.00743968792275]
We propose LACE, a novel concept value bottleneck model for controllable text recommendations.
LACE represents each user with a succinct set of human-readable concepts.
It learns personalized representations of the concepts based on user documents.
arXiv Detail & Related papers (2023-04-09T14:52:18Z) - Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform.
Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online.
Intent modeling is thus critical for understanding users and optimizing long-term user experience.
arXiv Detail & Related papers (2022-11-17T19:00:24Z) - Introducing a Framework and a Decision Protocol to Calibrate Recommender
Systems [0.0]
This paper proposes an approach to create recommendation lists with a calibrated balance of genres.
The main claim is that calibration can contribute positively to generate fairer recommendations.
We propose a conceptual framework and a decision protocol to generate more than one thousand combinations of calibrated systems.
arXiv Detail & Related papers (2022-04-07T19:30:55Z) - A Survey on Neural Recommendation: From Collaborative Filtering to
Content and Context Enriched Recommendation [70.69134448863483]
Research in recommendation has shifted to inventing new recommender models based on neural networks.
In recent years, we have witnessed significant progress in developing neural recommender models.
arXiv Detail & Related papers (2021-04-27T08:03:52Z) - Towards Comprehensive Recommender Systems: Time-Aware
UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network
Data [33.17802459749589]
We propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues.
We show that the proposed solution is superior in terms of accuracy, novelty and diversity.
Experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.
arXiv Detail & Related papers (2020-08-25T08:08:03Z) - Reward Constrained Interactive Recommendation with Natural Language
Feedback [158.8095688415973]
We propose a novel constraint-augmented reinforcement learning (RL) framework to efficiently incorporate user preferences over time.
Specifically, we leverage a discriminator to detect recommendations violating user historical preference.
Our proposed framework is general and is further extended to the task of constrained text generation.
arXiv Detail & Related papers (2020-05-04T16:23:34Z) - A Survey on Knowledge Graph-Based Recommender Systems [65.50486149662564]
We conduct a systematical survey of knowledge graph-based recommender systems.
We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation.
We introduce datasets used in these works.
arXiv Detail & Related papers (2020-02-28T02:26:30Z)
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.