Aspect-driven User Preference and News Representation Learning for News Recommendation

• URL: http://arxiv.org/abs/2110.05792v1
• Date: Tue, 12 Oct 2021 07:38:54 GMT
• Title: Aspect-driven User Preference and News Representation Learning for News Recommendation
• Authors: Rongyao Wang, Wenpeng Lu, Shoujin Wang, Xueping Peng, Hao Wu and Qian Zhang
• Abstract summary: News recommender systems usually learn topic-level representations of users and news for recommendation. We propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preference and news representation learning.
• Score: 9.187076140490902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news. Most of existing news recommender systems usually learn topic-level representations of users and news for recommendation, and neglect to learn more informative aspect-level features of users and news for more accurate recommendation. As a result, they achieve limited recommendation performance. Aiming at addressing this deficiency, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preference and news representation learning. Here, \textit{news aspect} is fine-grained semantic information expressed by a set of related words, which indicates specific aspects described by the news. In ANRS, \textit{news aspect-level encoder} and \textit{user aspect-level encoder} are devised to learn the fine-grained aspect-level representations of user's preferences and news characteristics respectively, which are fed into \textit{click predictor} to judge the probability of the user clicking the candidate news. Extensive experiments are done on the commonly used real-world dataset MIND, which demonstrate the superiority of our method compared with representative and state-of-the-art methods.

Related papers

• 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)
• IGNiteR: News Recommendation in Microblogging Applications (Extended Version) [3.2108350580418166]
  We propose a deep-learning based approach that is diffusion and influence-aware, called Influence-Graph News Recommender (IGNiteR) To represent the news, a multi-level attention-based encoder is used to reveal the different interests of users. We perform extensive experiments on two real-world datasets, showing that IGNiteR outperforms the state-of-the-art deep-learning based news recommendation methods.
  arXiv  Detail & Related papers  (2022-10-04T22:33:58Z)
• Two-Stage Neural Contextual Bandits for Personalised News Recommendation [50.3750507789989]
  Existing personalised news recommendation methods focus on exploiting user interests and ignores exploration in recommendation. We build on contextual bandits recommendation strategies which naturally address the exploitation-exploration trade-off. We use deep learning representations for users and news, and generalise the neural upper confidence bound (UCB) policies to generalised additive UCB and bilinear UCB.
  arXiv  Detail & Related papers  (2022-06-26T12:07:56Z)
• Quality-aware News Recommendation [92.67156911466397]
  existing news recommendation methods mainly aim to optimize news clicks while ignoring the quality of news they recommended. We propose a quality-aware news recommendation method named QualityRec that can effectively improve the quality of recommended news.
  arXiv  Detail & Related papers  (2022-02-28T08:25:58Z)
• Neural News Recommendation with Event Extraction [0.0]
  A key challenge of online news recommendation is to help users find articles they are interested in. Traditional news recommendation methods usually use single news information, which is insufficient to encode news and user representation. We propose an Event Extraction-based News Recommendation framework to overcome these shortcomings.
  arXiv  Detail & Related papers  (2021-11-09T11:56:38Z)
• Why Do We Click: Visual Impression-aware News Recommendation [108.73539346064386]
  This work is inspired by the fact that users make their click decisions mostly based on the visual impression they perceive when browsing news. We propose to capture such visual impression information with visual-semantic modeling for news recommendation. In addition, we inspect the impression from a global view and take structural information, such as the arrangement of different fields and spatial position of different words on the impression.
  arXiv  Detail & Related papers  (2021-09-26T16:58:14Z)
• Neural News Recommendation with Collaborative News Encoding and Structural User Encoding [18.407727437603178]
  We propose a news recommendation framework consisting of collaborative news encoding (CNE) and structural user encoding (SUE) Experiment results on the MIND dataset validate the effectiveness of our model to improve the performance of news recommendation.
  arXiv  Detail & Related papers  (2021-09-02T07:16:42Z)
• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion [77.21442487537139]
  Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference.
  arXiv  Detail & Related papers  (2020-07-08T11:14:23Z)
• Graph Enhanced Representation Learning for News Recommendation [85.3295446374509]
  We propose a news recommendation method which can enhance the representation learning of users and news. In our method, users and news are both viewed as nodes in a bipartite graph constructed from historical user click behaviors.
  arXiv  Detail & Related papers  (2020-03-31T15:27:31Z)

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.