Recommending Short-lived Dynamic Packages for Golf Booking Services
- URL: http://arxiv.org/abs/2103.07779v1
- Date: Sat, 13 Mar 2021 19:48:04 GMT
- Title: Recommending Short-lived Dynamic Packages for Golf Booking Services
- Authors: Robin Swezey, Young-joo Chung
- Abstract summary: We introduce an approach to recommending short-lived dynamic packages for golf booking services.
The first is the short life of the items, which puts the system in a state of a permanent cold start.
The second is the uninformative nature of the package attributes, which makes clustering or figuring latent packages challenging.
- Score: 0.5076419064097732
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce an approach to recommending short-lived dynamic packages for
golf booking services. Two challenges are addressed in this work. The first is
the short life of the items, which puts the system in a state of a permanent
cold start. The second is the uninformative nature of the package attributes,
which makes clustering or figuring latent packages challenging. Although such
settings are fairly pervasive, they have not been studied in traditional
recommendation research, and there is thus a call for original approaches for
recommender systems. In this paper, we introduce a hybrid method that leverages
user analysis and its relation to the packages, as well as package pricing and
environmental analysis, and traditional collaborative filtering. The proposed
approach achieved appreciable improvement in precision compared with baselines.
Related papers
- Personalized Diffusion Model Reshapes Cold-Start Bundle Recommendation [2.115789253980982]
We propose a new approach to generate a bundle in distribution space for each user to tackle the cold-start challenge.<n>DisCo relies on a personalized Diffusion backbone, enhanced by disentangled aspects for the user's interest.<n>DisCo outperforms five comparative baselines by a large margin on three real-world datasets.
arXiv Detail & Related papers (2025-05-20T20:52:31Z) - Language-Model Prior Overcomes Cold-Start Items [14.370472820496802]
The growth ofRecSys is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming.
Existing solutions for the cold-start problem, such as content-based recommenders and hybrid methods, leverage item metadata to determine item similarities.
This paper introduces a novel approach for cold-start item recommendation that utilizes the language model (LM) to estimate item similarities.
arXiv Detail & Related papers (2024-11-13T22:45:52Z) - 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) - End-to-End Learnable Item Tokenization for Generative Recommendation [51.82768744368208]
We propose ETEGRec, a novel End-To-End Generative Recommender by seamlessly integrating item tokenization and generative recommendation.
Our framework is developed based on the dual encoder-decoder architecture, which consists of an item tokenizer and a generative recommender.
arXiv Detail & Related papers (2024-09-09T12:11:53Z) - Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning [70.22819290458581]
Reinforcement learning with human feedback (RLHF) is a widely adopted approach in current large language model pipelines.
Our approach introduces two key innovations: (1) on-policy query to avoid OOD and imbalance issues in seed data, and (2) active learning to select the most informative data for preference queries.
arXiv Detail & Related papers (2024-07-02T10:09:19Z) - Impression-Aware Recommender Systems [57.38537491535016]
Novel data sources bring new opportunities to improve the quality of recommender systems.
Researchers may use impressions to refine user preferences and overcome the current limitations in recommender systems research.
We present a systematic literature review on recommender systems using impressions.
arXiv Detail & Related papers (2023-08-15T16:16:02Z) - A Sequence-Aware Recommendation Method Based on Complex Networks [1.385805101975528]
We build a network model from data and then use it to predict the user's subsequent actions.
The proposed method is implemented and tested experimentally on a large dataset.
arXiv Detail & Related papers (2022-09-30T16:34:39Z) - A Review on Pushing the Limits of Baseline Recommendation Systems with
the integration of Opinion Mining & Information Retrieval Techniques [0.0]
Recommendation Systems allow users to identify trending items among a community while being timely and relevant to the user's expectations.
Deep Learning methods have been brought forward to achieve better quality recommendations.
Researchers have tried to expand on the capabilities of standard recommendation systems to provide the most effective recommendations.
arXiv Detail & Related papers (2022-05-03T22:13:33Z) - Generating Self-Serendipity Preference in Recommender Systems for
Addressing Cold Start Problems [9.281057513518498]
serendipity-oriented recommender system generates users' self-serendipity preferences to enhance recommendation performance.
Model extracts users' interest and satisfaction preferences, generates virtual but convincible neighbors' preferences from themselves, and achieves their self-serendipity preference.
arXiv Detail & Related papers (2022-04-27T01:29:47Z) - Trust your neighbors: A comprehensive survey of neighborhood-based
methods for recommender systems [16.874144306491477]
Collaborative recommendation approaches based on nearest-neighbors are still highly popular today due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations.
This chapter offers a comprehensive survey of neighborhood-based methods for the item recommendation problem.
arXiv Detail & Related papers (2021-09-09T23:16:39Z) - Exploration in two-stage recommender systems [79.50534282841618]
Two-stage recommender systems are widely adopted in industry due to their scalability and maintainability.
A key challenge of this setup is that optimal performance of each stage in isolation does not imply optimal global performance.
We propose a method of synchronising the exploration strategies between the ranker and the nominators.
arXiv Detail & Related papers (2020-09-01T16:52:51Z) - SetRank: A Setwise Bayesian Approach for Collaborative Ranking from
Implicit Feedback [50.13745601531148]
We propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to accommodate the characteristics of implicit feedback in recommender system.
Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons.
We also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to $sqrtM/N$.
arXiv Detail & Related papers (2020-02-23T06:40:48Z)
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.