Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation

Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recommendation.Meanwhile,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation lists.However,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news recommendations.In this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity.Unlike most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the model.First,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result.Specifically,we focus on the relationship between the original clicked news and the augmented clicked news.Moreover,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results.Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire 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 traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

FedNRM:A Federal Personalized News Recommendation Model Achieving User Privacy Protection

In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.

Personalized News Recommendation Based on the Text and Image Integration

The personalized news recommendation has been very popular in the news recommendation field.In most research,the picture information in the news is ignored,but the information conveyed to the users through pictures is more intuitive and more likely to affect the users’reading interests than the one in the textual form.Therefore,in this paper,a model that combines images and texts in the news is proposed.In this model,the new tags are extracted from the images and texts in the news,and based on these new tags,an adaptive tag(AT)algorithm is proposed.The AT algorithm selects the tags the user is interested in based on the user feedback.In particular,the AT algorithm can predict tags that a user may be interested in with the help of the tag correlation graph without any user feedback.The proposed AT algorithm is verified by experiments.The experimental results verified the AT algorithm regarding three evaluation indexes F1-score(F1),area under curve(AUC)and mean reciprocal rank(MRR).The recommended effect of the proposed algorithm is found to be better than those of the various baseline algorithms on real-world datasets.

News Recommendation System Based on Topic Embedding and Knowledge Embedding

News recommendation system is designed to deal with massive news and provide personalized recommendations for users.Accurately capturing user preferences and modeling news and users is the key to news recommendation.In this paper,we propose a new framework,news recommendation system based on topic embedding and knowledge embedding(NRTK).NRTK handle news titles that users have clicked on from two perspectives to obtain news and user representation embedding:1)extracting explicit and latent topic features from news and mining users’preferences for them in historical behaviors;2)extracting entities and propagating users’potential preferences in the knowledge graph.Experiments in a real-world dataset validate the effectiveness and efficiency of our approach.

Personalized News Recommendation:A Review and an Experimental Investigation

Online news articles,as a new format of press releases,have sprung up on the Internet.With its convenience and recency,more and more people prefer to read news online instead of reading the paper-format press releases.However,a gigantic amount of news events might be released at a rate of hundreds,even thousands per hour.A challenging problem is how to efficiently select specific news articles from a large corpus of newly-published press releases to recommend to individual readers,where the selected news items should match the reader＇s reading preference as much as possible.This issue refers to personalized news recommendation.Recently,personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world.Existing personalized news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information.A variety of techniques have been proposed to tackle personalized news recommendation,including content-based,collaborative filtering systems and hybrid versions of these two.In this paper,we provide a comprehensive investigation of existing personalized news recommenders.We discuss several essential issues underlying the problem of personalized news recommendation,and explore possible solutions for performance improvement.Further,we provide an empirical study on a collection of news articles obtained from various news websites,and evaluate the effect of different factors for personalized news recommendation.We hope our discussion and exploration would provide insights for researchers who are interested in personalized news recommendation.

News recommendation based on time factor and word embedding

Existing algorithms of news recommendations lack in depth analysis of news texts and timeliness. To address these issues, an algorithm for news recommendations based on time factor and word embedding(TFWE) was proposed to improve the interpretability and precision of news recommendations. First, TFWE used term frequency-inverse document frequency(TF-IDF) to extract news feature words and used the bidirectional encoder representations from transformers(BERT) pre-training model to convert the feature words into vector representations. By calculating the distance between the vectors, TFWE analyzed the semantic similarity to construct a user interest model. Second, considering the timeliness of news, a method of calculating news popularity by integrating time factors into the similarity calculation was proposed. Finally, TFWE combined the similarity of news content with the similarity of collaborative filtering(CF) and recommended some news with higher rankings to users. In addition, results of the experiments on real dataset showed that TFWE significantly improved precision, recall, and F1 score compared to the classic hybrid recommendation algorithm.

Application of News Features in NewsRecommendation Methods: A Survey

In recent years, many traditional news websites developedcorresponding recommendation systems to cater to readers’ interestsand news recommendation systems are widely applied in traditional PCsand mobile devices. News recommendation system has become a criticalresearch hotspot in the field of recommendation system. As Newscontains more text information, it is more helpful to improve the recommendationeffect to obtain the content related to news features (location,time, events) from the news. This survey summarizes news features-basedrecommendation methods including location-based news recommendationmethods, time-based news recommendation methods, events-basednews recommendation methods. It helps researchers to know the applicationof news features in news recommendation methods. Also, thissuvery summarizes the challenges faced by the news recommendationsystem and the future research direction.