Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2207.12883v1
• Date: Tue, 19 Jul 2022 21:04:31 GMT
• Title: Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis
• Authors: Elliot Dang, Zheyuan Hu, Tong Li
• Abstract summary: Collaborative Filtering(CF) recommender is a crucial application in the online market and ecommerce. Existing methods address the data sparsity issue by applying token-level sentiment analysis that translate text review into sentiment scores as a complement of the user rating. This paper attempts to optimize the sentiment analysis with advanced NLP models including BERT and RoBERTa.
• Score: 4.123009513488148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative Filtering(CF) recommender is a crucial application in the online market and ecommerce. However, CF recommender has been proven to suffer from persistent problems related to sparsity of the user rating that will further lead to a cold-start issue. Existing methods address the data sparsity issue by applying token-level sentiment analysis that translate text review into sentiment scores as a complement of the user rating. In this paper, we attempt to optimize the sentiment analysis with advanced NLP models including BERT and RoBERTa, and experiment on whether the CF recommender has been further enhanced. We build the recommenders on the Amazon US Reviews dataset, and tune the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as well as the new prompt-based learning paradigm. Experimental result shows that the recommender enhanced with the sentiment ratings predicted by the fine-tuned RoBERTa has the best performance, and achieved 30.7% overall gain by comparing MAP, NDCG and precision at K to the baseline recommender. Prompt-based learning paradigm, although superior to traditional fine-tune paradigm in pure sentiment analysis, fail to further improve the CF recommender.

Related papers

• Preference Diffusion for Recommendation [50.8692409346126]
  We propose PreferDiff, a tailored optimization objective for DM-based recommenders. PreferDiff transforms BPR into a log-likelihood ranking objective to better capture user preferences. It is the first personalized ranking loss designed specifically for DM-based recommenders.
  arXiv  Detail & Related papers  (2024-10-17T01:02:04Z)
• RosePO: Aligning LLM-based Recommenders with Human Values [38.029251417802044]
  We propose a general framework -- Recommendation with smoothing personalized Preference Optimization (RosePO) RosePO better aligns with customized human values during the post-training stage. Evaluation on three real-world datasets demonstrates the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-10-16T12:54:34Z)
• Neural Graph Collaborative Filtering Using Variational Inference [19.80976833118502]
  We introduce variational embedding collaborative filtering (GVECF) as a novel framework to incorporate representations learned through a variational graph auto-encoder. Our proposed method achieves up to 13.78% improvement in the recall over the test data.
  arXiv  Detail & Related papers  (2023-11-20T15:01:33Z)
• Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation [52.62492168507781]
  We propose a novel benchmark called Fairness of Recommendation via LLM (FaiRLLM) This benchmark comprises carefully crafted metrics and a dataset that accounts for eight sensitive attributes. By utilizing our FaiRLLM benchmark, we conducted an evaluation of ChatGPT and discovered that it still exhibits unfairness to some sensitive attributes when generating recommendations.
  arXiv  Detail & Related papers  (2023-05-12T16:54:36Z)
• Improving Recommendation Fairness via Data Augmentation [66.4071365614835]
  Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes. In this paper, we study how to improve recommendation fairness from the data augmentation perspective.
  arXiv  Detail & Related papers  (2023-02-13T13:11:46Z)
• On the Theories Behind Hard Negative Sampling for Recommendation [51.64626293229085]
  We offer two insightful guidelines for effective usage of Hard Negative Sampling (HNS) We prove that employing HNS on the Personalized Ranking (BPR) learner is equivalent to optimizing One-way Partial AUC (OPAUC) These analyses establish the theoretical foundation of HNS in optimizing Top-K recommendation performance for the first time.
  arXiv  Detail & Related papers  (2023-02-07T13:57:03Z)
• CausPref: Causal Preference Learning for Out-of-Distribution Recommendation [36.22965012642248]
  The current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios. We propose to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref. Our approach surpasses the benchmark models significantly under types of out-of-distribution settings.
  arXiv  Detail & Related papers  (2022-02-08T16:42:03Z)
• Learning to Learn a Cold-start Sequential Recommender [70.5692886883067]
  The cold-start recommendation is an urgent problem in contemporary online applications. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR. metaCSR holds the ability to learn the common patterns from regular users' behaviors.
  arXiv  Detail & Related papers  (2021-10-18T08:11:24Z)
• Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality [6.197934754799158]
  We model the learning characteristics of the Bayesian Personalized Ranking (BPR) and APR optimization frameworks. We show that APR amplifies the popularity bias more than BPR due to an unbalanced number of received positive updates from short-head items.
  arXiv  Detail & Related papers  (2021-07-29T10:22:20Z)
• Reenvisioning Collaborative Filtering vs Matrix Factorization [65.74881520196762]
  Collaborative filtering models based on matrix factorization and learned similarities using Artificial Neural Networks (ANNs) have gained significant attention in recent years. Announcement of ANNs within the recommendation ecosystem has been recently questioned, raising several comparisons in terms of efficiency and effectiveness. We show the potential these techniques may have on beyond-accuracy evaluation while analyzing effect on complementary evaluation dimensions.
  arXiv  Detail & Related papers  (2021-07-28T16:29:38Z)
• Implicit Feedback Deep Collaborative Filtering Product Recommendation System [1.6651146574124562]
  Collaborative Filtering (CF) approaches with latent variable methods were studied to capture important hidden variations of the sparse customer purchasing behaviours. The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations. The work shown in this paper provides techniques the Company can use to provide product recommendations to enhance revenues, attract new customers, and gain advantages over competitors.
  arXiv  Detail & Related papers  (2020-09-08T19:30:14Z)

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.