Study On User Modeling And Personalized Recommendation In Heterogeneous Environments

In the era of information explosion,personalized recommender systems have become indispensable online services.Based on the results of user preference modeling,recommender systems will extract information that may be of interest to every user from the massive information and recommend it to the user.Better user modeling will result in a better understanding of user interests,and then recommender systems can provide users with better recommendation results to meet their demands based on their interests.Since users often use multiple services and applications on the Internet,they will generate different types of heterogeneous information: multi-modality,cross-domain or cross-platform information.As heterogeneous information is derived from heterogeneous environments,the distributions and characteristics of it are often different.Only by comprehensively using this information can achieve better users modeling and recommendation results.Thus,how to integrate these multi-source heterogeneous data has become a big challenge in the research of user modeling and recommender systems,but it also brings new opportunities: Firstly,existing user modeling studies usually based on only single modality data,especially textual data.While other modality information generated by users are often ignored.Secondly,most previous recommendation methods focus on making use of existing features on the recommender systems,which often suffer from the cold-start problem.Thirdly,knowledge graph,which contains a large amount of knowledge information,can be helpful for the recommendation.While the structured knowledge information and user behavior information are heterogeneous information.In order to overcome these challenges,this paper conduct research on user modeling and recommendation under heterogeneous environments:(1)We start our research from modeling the objective attributes of users,the proposed multi-modality multi-granularity algorithm achieves 90.4% role recognition accuracy on real datasets.Besides,inspired by psychology theories,we modify some algorithms to incorporate user role features and achieve good performance in experiments.(2)We try to model users' subjective preference more accurately with cross-platform user features.A new framework which is able to incorporate cross-platform heterogeneous information is proposed.The designed method is flexible to work with existing rating prediction algorithms.The enhanced algorithms get significant improvements in Douban datasets,the root mean square error is reduced by4.2%.Besides,our methods are able to cope with cold start users.(3)We are committed to integrating knowledge graph and reasoning with recommender systems.We combine the heterogeneous information from knowledge graph to mine the features between items,and then the association feature enhanced recommendation algorithms outperform the original methods significantly,the averaged recall@5 improvement is 4.39% on Amazon dataset.We focus on how to effectively integrate heterogeneous data to improve user modeling and recommendation performance in this study.The main contributions are as follows:(1)A multi-granularity role recognition framework with multi-modality information is designed for the first time,and user role features are introduced for user preference and attitude prediction;(2)The proposed cross-platform cross-domain heterogeneous recommendation algorithm is able to deal with the cold-start problem,and our study verify that “off-topic" information is also helpful for preference modeling;(3)The proposed framework that combines knowledge information is able to give an explanation of the recommended results.These proposed methods have good robustness and scalability,and can effectively improve the performance of recommendation systems in real-scenarios.