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recommendations based on user effective point-of-interest path

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第10期10卷 2887-2899页

作者： Zhou, Guoqiang Zhang, Shuai Fan, Yi Li, Jingjin Yao, Wenbo Liu, Hongfang Nanjing Univ Posts & Telecommun Coll Comp Sci Nanjing 210003 Jiangsu Peoples R China

Point-of-interest (POI) recommendation has become an important service in location-based social networks. Existing recommendation algorithms provide users with a diverse pool of POIs. However, these algorithms tend to generate a list of unrelated POIs that user cannot continuously visit due to lack of appropriate associations. In this paper, we first proposed a concept that can recommend POIs by considering both category diversity features of POIs and possible associations of POIs. Then, we developed a top-k POI recommendation model based on effective path coverage. Moreover, considering this model has been proven to be a NP-hard problem, we developed a dynamic optimization algorithm to provide an approximate solution. Finally, we compared it with two popular algorithms by using two real-world datasets, and found that our proposed algorithm has better performance in terms of diversity and precision.

关键词： Location-based social networks Effective path coverage recommendation algorithm Point-of-interest

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A novel multi-objective evolutionary algorithm for recommendation systems

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2017年 103卷 53-63页

作者： Cui, Laizhong Ou, Peng Fu, Xianghua Wen, Zhenkun Lu, Nan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

Nowadays, the recommendation algorithm has been used in lots of information systems and Internet applications. The recommendation algorithm can pick out the information that users are interested in. However, most traditional recommendation algorithms only consider the precision as the evaluation metric of the performance. Actually, the metrics of diversity and novelty are also very important for recommendation. Unfortunately, there is a conflict between precision and diversity in most cases. To balance these two metrics, some multi-objective evolutionary algorithms are applied to the recommendation algorithm. In this paper, we firstly put forward a kind of topic diversity metric. Then, we propose a novel multi-objective evolutionary algorithm for recommendation systems, called PMOEA. In PMOEA, we present a new probabilistic genetic operator. Through the extensive experiments, the results demonstrate that the combination of PMOEA and the recommendation algorithm can achieve a good balance between precision and diversity. (C) 2016 Elsevier Inc. All rights reserved.

关键词： recommendation algorithm Multi-objective optimization Topic diversity Genetic operator

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TIME-SENSITIVE COLLABORATIVE FILTERING algorithm WITH FEATURE STABILITY

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COMPUTING AND INFORMATICS 2020年第1-2期39卷 141-155页

作者： Pang, Shanchen Yu, Shihang Li, Guiling Qiag, Sibo Wang, Min Shandong Univ Sci & Technol 579 Qianwangang Rd Qingdao Peoples R China Shandong Univ Sci & Technol Acad Math & Syst Sci 579 Qianwangang Rd Qingdao Peoples R China

In the recommendation system, the collaborative filtering algorithm is widely used. However, there are lots of problems which need to be solved in recommendation field, such as low precision, the long tail of items. In this paper, we design an algorithm called FSTS for solving the low precision and the long tail. We adopt stability variables and time-sensitive factors to solve the problem of user's interest drift, and improve the accuracy of prediction. Experiments show that, compared with Item-CF, the precision, the recall, the coverage and the popularity have been significantly improved by FSTS algorithm. At the same time, it can mine long tail items and alleviate the phenomenon of the long tail.

关键词： Collaborative filtering recommendation algorithm long tail time-sensitive

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Design and Implementation of the Cross-Harmonic Recommender System Based on Spark 3rd

Design and Implementation of the Cross-Harmonic Recommender ...

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3rd European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Huang Jie Liu ChangSheng Liu ChengLi Airforce Aviat Repair Inst Technol Dept Aviat Elect Equipment Maintenance Changsha 410014 Hunan Peoples R China Hunan Key Lab Intelligent Informat Percept & Proc Zhuzhou 412007 Hunan Peoples R China Univ Leon Sch Engn Comp & Aviat Leon 24071 Spain

ISBN: (纸本)9783030364052;9783030364045

With the rapid development of information technology, information overload has become an important challenge of Internet. In order to alleviate the growing contradiction between users and massive data, the researchers proposed the concept of the cross-harmonic recommender system. By analyzing characteristic of datasets, recommendation algorithms and method for weight calculation, we introduced a fast and general engine for large-scale data processing and implemented the cross-harmonic recommender system based on Spark, aiming at improving accuracy, diversity and efficiency of the recommender system.

关键词： Spark Hybrid recommendation recommendation algorithm

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A movie recommendation algorithm based on genre correlations

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第9期39卷 8079-8085页

作者： Choi, Sang-Min Ko, Sang-Ki Han, Yo-Sub Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

Since the late 20th century, the number of Internet users has increased dramatically, as has the number of Web searches performed on a daily basis and the amount of information available. A huge amount of new information is transferred to the Web on a daily basis. However, not all data are reliable and valuable, which implies that it may become more and more difficult to obtain satisfactory results from Web searches. We often iterate searches several times to find what we are looking for. To solve this problem, researchers have suggested the use of recommendation systems. Instead of searching for the same information several times, a recommendation system proposes relevant information. In the Web 2.0 era, recommendation systems often rely on collaborative filtering by users. In general, a collaborative filtering approach based on user information such as gender, location, or preference is effective. However, the traditional approach can fail due to the cold-start problem or the sparsity problem, because initial user information is required for this approach to be effective. Recently, several attempts have been made to tackle these collaborative filtering problems. One such attempt used category correlations of contents. For instance, a movie has genre information provided by movie experts and directors. This category information is more reliable than user ratings. Moreover, newly created content always has category information, allowing avoidance of the cold-start problem. In this study, we consider a movie recommendation system and improve the previous algorithms based on genre correlations to correct its shortcomings. We also test the modified algorithm and analyze the results with respect to two characteristics of genre correlations. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： recommendation algorithm Genre correlation Cold-start problem Sparsity problem

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Collaborative Filtering algorithm based on Linear Regression Filling 3

Collaborative Filtering Algorithm based on Linear Regression...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Huang, Meigen Wang, Yu Zhou, Lihan Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9781538662434

In the collaborative filtering recommendation algorithm, sparse user rating data may result in inaccurate similarity calculation between users. To solve this problem, this paper proposes a method of filling unrated data on the user-item rating matrix by using linear regression model. Firstly, this method selects the average of user historical ratings and the average of item historical ratings as the features, selects the user's actual rating as the label, and trains the linear regression model of rating prediction for each user. Then, use the model to predict and fill the user's unrated data. Finally, use the traditional collaborative filtering algorithm for rating prediction on the filled user-item rating matrix. Experimental results show that the improved collaborative filtering recommendation algorithm can alleviate the data sparsity, find more reliable user neighbors, and improve the accuracy of rating prediction.

关键词： collaborative filtering linear regression matrix filling recommendation algorithm rating prediction

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Latent Factor Models Fusing User & Item Attributes

Latent Factor Models Fusing User & Item Attributes

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Wang, Huiwei Zhao, Yong Wang, Qingya Zhou, Bo Southwest Univ Sch Comp Sci & Engn Chongqing 400715 Peoples R China Southwest Univ Coll Life Sci Westa Coll Chongqing 400715 Peoples R China Chongqing Jiaotong Univ Coll Math & Stat Chongqing 400074 Peoples R China

ISBN: (纸本)9781728124858

Data sparsity, cold-start, and suboptimal recommendation for local users or items have been recognized as the most crucial three challenges in the latent factor model (LFM) for recommender systems. This paper proposes an approach that integrates the User-Item attributes into the classical LFM named UILFM focusing on above challenges. First, for the problem of data sparsity and cold-start, we develop an online learning algorithm to update the weights of user or item attribute for identifying the importance of different attributes. By aggregating the users and items based on their similar attributes, we obtain the local neighbor group which makes it possible for recommender to estimate some missing ratings based on adjacent user's ratings towards items and adjacent item's ratings. By introducing the convex mixed-parameters, we combine the estimate ratings with the classical LFM to predict the missing entries of the high-dimensional and sparse (HiDS) matrix for further closing the true ratings and reducing matrix sparsity. Second, for the suboptimal recommendation problem, we propose a new matrix filling (for missing ratings) method based on positive and negative samples, in which when the sparsity of the HiDS matrix is reduced to a threshold, the classical LFM will dominate the filling procedure, instead, the prediction based on neighbors' ratings remains a domination role. This method elegantly solves the suboptimal recommendation problem that the ratings of partial users are extremely sparse and the number of ratings per user are unbalanced. The proposed algorithm is tested by the MovieLens dataset, the results show that it promotes the recommendation accuracy compared with the classical LFM algorithm and the dimensionality reduction approaches as well as the collaborative filtering (CF) algorithms.

关键词： latent factor model recommendation algorithm user and item attributes cold start

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The dynamic competitive recommendation algorithm in social network services

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INFORMATION SCIENCES 2012年第1期187卷 1-14页

作者： Yu, Seok Jong Sookmyung Womens Univ Dept Comp Sci Seoul 140742 South Korea

As the number of Twitter users exceeds 175 million and the scale of social network increases, it is facing with a challenge to how to help people find right people and information conveniently. For this purpose, current social network services are adopting personalized recommender systems. Existing recommendation algorithms largely depend on one of content-based algorithm, collaborative filtering, or influential ranking analysis. However, these algorithms tend to suffer from the performance fluctuation phenomenon in common whenever an active user changes, and it is due to the diversities of personal characteristics such as the local social graph size, the number of followers, or sparsity of profile content. To overcome this limitation and to provide consistent and stable recommendation in social networks, this study proposes the dynamic competitive recommendation algorithm based on the competition of multiple component algorithms. This study shows that it outperforms previous approaches through performance evaluation on actual Twitter dataset. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Social network service Recommender system recommendation algorithm Twitter PageRank

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An Improved Neighborhood-Aware Unified Probabilistic Matrix Factorization recommendation

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WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 3121-3140页

作者： Cao, Yulin Li, Wenli Zheng, Dongxia Dalian Univ Technol Fac Econ & Management Dalian 116000 Liaoning Peoples R China Dalian Polytech Univ Sch Management Dalian 116000 Liaoning Peoples R China Dalian Neusoft Informat Univ Dept Software Engn Dalian 116000 Liaoning Peoples R China

recommendation systems require sufficient information to provide proper recommendations. Both rating and tagging information can be used in social tagging systems. Many recommendation systems consider the relationships between users, items and tags, which affect the recommendation results. To address this issue, this paper proposes a neighborhood-aware unified probabilistic matrix factorization recommendation model that fuses social tagging. In the proposed approach, the similarities between users and items are first calculated by using tags to make neighborhood selections. Then, a user-item rating matrix, a user-tag tagging matrix, an item-tag correlation matrix and a unified probabilistic matrix factorization are constructed to obtain the latent feature vectors of three matrices to be recommended to users by optimizing the training parameters. In the experiments, the proposed model is compared with three other collaborative filtering approaches on the MovieLens dataset to evaluate its performance. The experimental results demonstrate that the proposed model uses the tag semantics effectively and improves the recommendation quality.

关键词： Social tagging Neighborhood-aware Unified probability matrix factorization recommendation algorithm

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Research on Retrieval Ranking Based on Deep Reinforcement Learning 6

Research on Retrieval Ranking Based on Deep Reinforcement Le...

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6th International Conference on Information Science and Control Engineering (ICISCE)

作者： Zhang, Kun Lin, Min Li, Yanling Inner Mongolia Normal Univ Sch Comp Sci & Technol Hohhot Peoples R China

ISBN: (纸本)9781728157122

Retrieval ranking technology is the core technology for evaluating information retrieval results. The advantages and disadvantages of retrieval ranking algorithms directly affect the retrieval effect of the system. The traditional retrieval ranking algorithm treats each ranking decision step independently. Moreover, the traditional retrieval ranking algorithm does not consider personalized retrieval ranking for different types of users. The recommendation algorithm based on deep reinforcement learning has made a lot of research on this problem, and it is a new attempt to apply the idea of recommendation algorithm in the retrieval ranking scene. In this paper, the related algorithms of recommendation and retrieval ranking algorithms based on deep reinforcement learning are reviewed in recent years. By considering the ranking process as the Markov decision process, using reinforcement learning to solve the problem of correlation between decision-making steps, an interactive retrieval model is constructed. Interactive retrieval can guide users to define their own needs, by introducing a personalized user simulator to simulate different types of environments, and using reinforcement learning to train personalized retrieval goals. Combining the retrieval rank with the recommendation algorithm will improve the retrieval effect of the user essentially.

关键词： interactive: personalized retrieval deep reinforcement learning recommendation algorithm Markov decision process

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