We lifted 7 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| xuan-zw/lkpnr | canonical | 7 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| aggregate_criteria | Ran | xuan-zw/lkpnr/NNR/aggregate_result.py pointer only (licence: NONE) · get_code("d46a7fdff12fd4b7") |
| construct_news_text | Ran | xuan-zw/lkpnr/LLM/get_item.py pointer only (licence: NONE) · get_code("1bf6a1550a765994") |
| dcg_score | Ran | xuan-zw/lkpnr/NNR/evaluate.py pointer only (licence: NONE) · get_code("43e79e8e0904d5d8") |
| is_number | Ran | xuan-zw/lkpnr/NNR/MIND_corpus.py pointer only (licence: NONE) · get_code("e18607dae38b30e2") |
| mrr_score | Ran | xuan-zw/lkpnr/NNR/evaluate.py pointer only (licence: NONE) · get_code("47a7b89c55c76c60") |
| ndcg_score | Ran | xuan-zw/lkpnr/NNR/evaluate.py pointer only (licence: NONE) · get_code("aaa4cece9f41ea15") |
| transform_term_vectors | Ran | xuan-zw/lkpnr/NNR/general_recommendation_methods/DSSM_util.py pointer only (licence: NONE) · get_code("fa9c18d8c9b9736c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Accurately recommending candidate news articles to users is a basic challenge faced by personalized news recommendation systems. Traditional methods are usually difficult to grasp the complex semantic information in news texts, resulting in unsatisfactory recommendation results. Besides, these traditional methods are more friendly to active users with rich historical behaviors. However, they can not effectively solve the "long tail problem" of inactive users. To address these issues, this research presents a novel general framework that combines Large Language Models (LLM) and Knowledge Graphs (KG) into semantic representations of traditional methods. In order to improve semantic understanding in complex news texts, we use LLMs' powerful text understanding ability to generate news representations containing rich semantic information. In addition, our method combines the information about news entities and mines high-order structural information through multiple hops in KG, thus alleviating the challenge of long tail distribution. Experimental results demonstrate that compared with various traditional models, the framework significantly improves the recommendation effect. The successful integration of LLM and KG in our framework has established a feasible path for achieving more accurate personalized recommendations in the news field. Our code is available at https://github.com/Xuan-ZW/LKPNR.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.12028")
get_code_for_paper("2308.12028")
have("2308.12028")
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