scholarly journals Tag-Aware Recommender System Based on Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiruo Zhao ◽  
Xiliang Chen ◽  
Zhixiong Xu ◽  
Lei Cao
Keyword(s):  
Reinforcement Learning ◽  
Recommender System ◽  
Complex Function ◽  
Cold Start ◽  
Sparse Data ◽  
Excellent Performance ◽  
Minimum Error ◽  
Reward Function ◽  
Function Design

Recently, the application of deep reinforcement learning in the recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns, and sparse data issues in the recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This study proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in the recommender system. Our experiment is carried out on the MovieLens dataset. The result shows that the DRL-based recommender system is superior than traditional algorithms in minimum error, and the application of tags have little effect on accuracy when making up for interpretability. In addition, the DRL-based recommender system has excellent performance on user cold start problems.

scholarly journals Tag-Aware Recommender System Based on Deep Reinforcement Learning

2021 ◽  
Author(s):  
Zhiruo Zhao ◽  
Lei Cao ◽  
Xiliang Chen ◽  
Zhixiong Xu
Keyword(s):  
Reinforcement Learning ◽  
Recommender System ◽  
Complex Function ◽  
Cold Start ◽  
Sparse Data ◽  
Excellent Performance ◽  
Minimum Error ◽  
Reward Function ◽  
Function Design

Recently, the application of deep reinforcement learning in recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns and sparse data issues in recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This paper proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in recommender system. Our experiment is carried out on MovieLens dataset. The result shows that, DRL based recommender system is superior than traditional algorithms in minimum error and the application of tags has little effect on accuracy when making up for interpretability. In addition, DRL based recommender system has excellent performance on user cold start problems.

A deep reinforcement learning based long-term recommender system

2021 ◽  
Vol 213 ◽  
pp. 106706
Author(s):  
Liwei Huang ◽  
Mingsheng Fu ◽  
Fan Li ◽  
Hong Qu ◽  
Yangjun Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Recommender System

scholarly journals Addressing Complete New Item Cold-Start Recommendation: A Niche Item-Based Collaborative Filtering via Interrelationship Mining

2019 ◽  
Vol 9 (9) ◽  
pp. 1894 ◽  
Author(s):  
Zhi-Peng Zhang ◽  
Yasuo Kudo ◽  
Tetsuya Murai ◽  
Yong-Gong Ren
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Cold Start ◽  
Binary Relations ◽  
Excellent Performance ◽  
Satisfactory Accuracy ◽  
Available Information ◽  
Item Attributes ◽  
New Item ◽  
Cold Start Problem

Recommender system (RS) can be used to provide personalized recommendations based on the different tastes of users. Item-based collaborative filtering (IBCF) has been successfully applied to modern RSs because of its excellent performance, but it is susceptible to the new item cold-start problem, especially when a new item has no rating records (complete new item cold-start). Motivated by this, we propose a niche approach which applies interrelationship mining into IBCF in this paper. The proposed approach utilizes interrelationship mining to extract new binary relations between each pair of item attributes, and constructs interrelated attributes to rich the available information on a new item. Further, similarity, computed using interrelated attributes, can reflect characteristics between new items and others more accurately. Some significant properties, as well as the usage of interrelated attributes, are provided in detail. Experimental results obtained suggest that the proposed approach can effectively solve the complete new item cold-start problem of IBCF and can be used to provide new item recommendations with satisfactory accuracy and diversity in modern RSs.

A novel movies recommendation algorithm based on reinforcement learning with DDPG policy

2020 ◽  
Vol 13 (1) ◽  
pp. 67-79
Author(s):  
Qiaoling Zhou
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Cold Start ◽  
Sparse Data ◽  
Local Optimum ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Content Type ◽  
Recommendation Algorithms ◽  
The Impact

PurposeEnglish original movies played an important role in English learning and communication. In order to find the required movies for us from a large number of English original movies and reviews, this paper proposed an improved deep reinforcement learning algorithm for the recommendation of movies. In fact, although the conventional movies recommendation algorithms have solved the problem of information overload, they still have their limitations in the case of cold start-up and sparse data.Design/methodology/approachTo solve the aforementioned problems of conventional movies recommendation algorithms, this paper proposed a recommendation algorithm based on the theory of deep reinforcement learning, which uses the deep deterministic policy gradient (DDPG) algorithm to solve the cold starting and sparse data problems and uses Item2vec to transform discrete action space into a continuous one. Meanwhile, a reward function combining with cosine distance and Euclidean distance is proposed to ensure that the neural network does not converge to local optimum prematurely.FindingsIn order to verify the feasibility and validity of the proposed algorithm, the state of the art and the proposed algorithm are compared in indexes of RMSE, recall rate and accuracy based on the MovieLens English original movie data set for the experiments. Experimental results have shown that the proposed algorithm is superior to the conventional algorithm in various indicators.Originality/valueApplying the proposed algorithm to recommend English original movies, DDPG policy produces better recommendation results and alleviates the impact of cold start and sparse data.

scholarly journals Inverse reinforcement learning in contextual MDPs

2021 ◽  
Author(s):  
Stav Belogolovsky ◽  
Philip Korsunsky ◽  
Shie Mannor ◽  
Chen Tessler ◽  
Tom Zahavy
Keyword(s):  
Reinforcement Learning ◽  
Optimization Problem ◽  
Decision Processes ◽  
Inverse Reinforcement Learning ◽  
Convex Optimization Problem ◽  
Reward Function ◽  
Dynamic Treatment Regime ◽  
Markov Decision ◽  
Dynamic Treatment ◽  
Recorded Data

AbstractWe consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the agent observes demonstrations from an optimal policy. The goal is to learn the reward mapping, such that the agent will act optimally even when encountering previously unseen contexts, also known as zero-shot transfer. We formulate this problem as a non-differential convex optimization problem and propose a novel algorithm to compute its subgradients. Based on this scheme, we analyze several methods both theoretically, where we compare the sample complexity and scalability, and empirically. Most importantly, we show both theoretically and empirically that our algorithms perform zero-shot transfer (generalize to new and unseen contexts). Specifically, we present empirical experiments in a dynamic treatment regime, where the goal is to learn a reward function which explains the behavior of expert physicians based on recorded data of them treating patients diagnosed with sepsis.

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