Summary of Glocim: Global-view Long Chain Interest Modeling For News Recommendation, by Zhen Yang et al.
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation
by Zhen Yang, Wenhui Wang, Tao Qi, Peng Zhang, Tianyun Zhang, Ru Zhang, Jianyi Liu, Yongfeng Huang
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for improving news recommendation systems, specifically addressing the challenge of modeling user interest to match candidate news articles. The authors focus on leveraging global click graph information to extract far-reaching linkages between users with similar interests. To achieve this, they introduce Global-view Long Chain Interests Modeling (GLoCIM), which combines local neighbor interest with long chain interest distilled from the global click graph. This method utilizes a long chain selection algorithm and encoder to obtain global-view long chain interest, which is then integrated with neighbor interest using a gated network. The final user representation is formed by aggregating this collaborative interest with local news category-enhanced representations. Experimental results on real-world datasets demonstrate the effectiveness of GLoCIM in improving news recommendation performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve how news articles are recommended to users based on their interests. The problem is that current methods don’t fully understand how people with similar interests connect each other. To solve this, the authors create a new way of analyzing large amounts of data about what people like and dislike. This method combines information from nearby users with information from more distant users who have similar tastes. It then uses this combined information to make better recommendations for each user. The results show that this approach is effective in improving news recommendation accuracy. |
Keywords
» Artificial intelligence » Encoder
