scholarly journals Sequential Recommendations on Board-Game Platforms

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 210 ◽  
Author(s):  
JaeWon Kim ◽  
JeongA Wi ◽  
SooJin Jang ◽  
YoungBin Kim
Keyword(s):  
Online Community ◽  
Large Scale ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Board Game ◽  
Board Games ◽  
Steady Growth ◽  
Long Time ◽  
User Data

The game market is an increasingly large industry. The board-game market, which is the most traditional in the game market, continues to show a steady growth. It is very important for both publishers and players to predict the propensity of users in this huge market and to recommend new games. Despite its importance, no study has been performed on board-game recommendation systems. In this study, we propose a method to build a deep-learning-based recommendation system using large-scale user data of an online community related to board games. Our study showed that new games can be effectively recommended for board-game users based on user big data accumulated for a long time. This is the first study to propose a personalized recommendation system for users in the board-game market and to introduce a provision of new large datasets for board-game users. The proposed dataset shares symmetric characteristics with other datasets and has shown its ability to be applied to various recommendation systems through experiments. Therefore, the dataset and recommendation system proposed in this study are expected to be applied for various studies in the field.

scholarly journals Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation

2020 ◽  
Author(s):  
Ruobing Xie ◽  
Zhijie Qiu ◽  
Jun Rao ◽  
Yi Liu ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Small Subset ◽  
Multiple Channels ◽  
Attention Network

Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https://github.com/zhijieqiu/ICAN.

Meta-Rule Based Recommender Systems for Educational Applications

2012 ◽  
pp. 211-231 ◽  
Author(s):  
Vicente Arturo Romero Zaldivar ◽  
Daniel Burgos ◽  
Abelardo Pardo
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Relevant Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Rule Based ◽  
User Data ◽  
Rule Sets ◽  
Rule Set ◽  
Educational Applications

Recommendation Systems are central in current applications to help the user find relevant information spread in large amounts of data. Most Recommendation Systems are more effective when huge amounts of user data are available. Educational applications are not popular enough to generate large amount of data. In this context, rule-based Recommendation Systems seem a better solution. Rules can offer specific recommendations with even no usage information. However, large rule-sets are hard to maintain, reengineer, and adapt to user preferences. Meta-rules can generalize a rule-set which provides bases for adaptation. In this chapter, the authors present the benefits of meta-rules, implemented as part of Meta-Mender, a meta-rule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach to Recommendation Systems.

Personalized Content Recommendation Engine for Web Publishing Services Using Textmining and Predictive Analytics

2017 ◽  
pp. 113-124
Author(s):  
Başar Öztayşi ◽  
Ahmet Tezcan Tekin ◽  
Cansu Özdikicioğlu ◽  
Kerim Caner Tümkaya
Keyword(s):  
Recommendation System ◽  
Predictive Analytics ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Web Contents ◽  
Interest Level ◽  
History Of ◽  
Content Recommendation ◽  
Publishing Services ◽  
Web Publishing

Recommendation systems have become very important especially for internet based business such as e-commerce and web publishing. While content based filtering and collaborative filtering are most commonly used groups in recommendation systems there are still researches for new approaches. In this study, a personalized recommendation system based on text mining and predictive analytics is proposed for a real world web publishing company. The approach given in this chapter first preprocesses existing web contents, integrate the structured data with history of a specific user and create an extended TDM for the user. Then this data is used for prediction of the users interest in new content. In order to reach that point, SVM, K-NN and Naïve Bayesian methods are used. Finally, the best performing method is used for determining the interest level of the user in a new content. Based on the forecasted interest levels the system recommends among the alternatives.

Research on Intelligent Recommendation Method and its Application on Internet Bookstore

2010 ◽  
Vol 121-122 ◽  
pp. 447-452
Author(s):  
Qing Zhang Chen ◽  
Yu Jie Pei ◽  
Yan Jin ◽  
Li Yan Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
The Internet

As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is implemented with ASP.NET in this article. The experimental results indicate that the Internet bookstore recommendation system is feasible.

scholarly journals A Hybrid Two-Phase Recommendation for Group-Buying E-commerce Applications

2019 ◽  
Vol 9 (15) ◽  
pp. 3141
Author(s):  
Li Bai ◽  
Mi Hu ◽  
Yunlong Ma ◽  
Min Liu
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Two Phase ◽  
Group Buying ◽  
Large Scale Data ◽  
User Clustering ◽  
Scale Data

The last two decades have witnessed an explosive growth of e-commerce applications. Existing online recommendation systems for e-commerce applications, particularly group-buying applications, suffer from scalability and data sparsity problems when confronted with exponentially increasing large-scale data. This leads to a poor recommendation effect of traditional collaborative filtering (CF) methods in group-buying applications. In order to address this challenge, this paper proposes a hybrid two-phase recommendation (HTPR) method which consists of offline preparation and online recommendation, combining clustering and collaborative filtering techniques. The user-item category tendency matrix is constructed after clustering items, and then users are clustered to facilitate personalized recommendation where items are generated by collaborative filtering technology. In addition, a parallelized strategy was developed to optimize the recommendation process. Extensive experiments on a real-world dataset were conducted by comparing HTPR with other three recommendation methods: traditional CF, user-clustering based CF, and item-clustering based CF. The experimental results show that the proposed HTPR method is effective and can improve the accuracy of online recommendation systems for group-buying applications.

Finding m-similar users in social networks using the m-representative skyline query

2017 ◽  
Vol 45 (3) ◽  
pp. 121-129
Author(s):  
Kuo-Cheng Ting ◽  
Ruei-Ping Wang ◽  
Yi-Chung Chen ◽  
Don-Lin Yang ◽  
Hsi-Min Chen
Keyword(s):  
Social Networks ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Skyline Query ◽  
Skyline Queries ◽  
Content Type ◽  
Target User ◽  
User Data ◽  
Treat All ◽  
Representative Skyline

Purpose Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach. Design/methodology/approach We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries. Findings By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach. Originality/value Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

An Enhanced Similarity Measure Used in Personalized Recommendation Algorithms

2010 ◽  
Vol 159 ◽  
pp. 671-675 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Rating Data ◽  
Measure Methods ◽  
Collaborative Filtering Algorithm

Personalized recommendation systems combine the data mining technology with users browse profile and provide recommendation set to user forecasted by their interests. Collaborative filtering algorithm is one of the most successful methods for building personalized recommendation system, and is extensively used in many fields to date. With the development of E-commerce, the magnitudes of users and items grow rapidly, resulting in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To alleviate the problem, an enhanced Pearson correlation similarity measure method is introduced in the personalized collaborative filtering recommendation algorithm. The approach considers the common correlation rating of users. The recommendation using the enhanced similarity measure can improve the neighbors influence in the course of recommendation and enhance the accuracy and the quality of recommendation systems effectively.

scholarly journals Revealing the impact of expertise on human planning with a two-player board game

2021 ◽  
Author(s):  
Bas van Opheusden ◽  
Gianni Galbiati ◽  
Ionatan Kuperwajs ◽  
Zahy Bnaya ◽  
Yunqi li ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Decision Makers ◽  
Board Game ◽  
Board Games ◽  
Ample Opportunity ◽  
Common Planning ◽  
Lower Complexity ◽  
The Impact ◽  
Robust Evidence

Do skilled decision-makers plan further into the future than novices? This question has been investigated for almost 75 years, traditionally by studying expert players in complex board games like chess. However, the complexity of these games poses a barrier to detailed modeling of human behavior. Conversely, common planning tasks in cognitive science are often lower-complexity and impose a ceiling for the depth to which any player can plan. Here, we investigate expertise by studying decision-making in a board game which is at the limit of complexity that can be precisely modeled using state-of-the-art statistical techniques, and which has ample opportunity for skilled players to plan deeply. We find robust evidence for increased planning depth with expertise in both laboratory and large-scale naturalistic data.

scholarly journals Sentiment Digitization Modeling for Recommendation System

2020 ◽  
Vol 12 (12) ◽  
pp. 5191
Author(s):  
Tae-Yeun Kim ◽  
Sung Bum Pan ◽  
Sung-Hwan Kim
Keyword(s):  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Quantitative Information ◽  
Personalized Recommendation ◽  
Rating Data ◽  
User Reviews ◽  
Lower Accuracy ◽  
Types Of Information

As the importance of providing personalized services increases, various studies on personalized recommendation systems are actively being conducted. Among the many methods used for recommendation systems, the most widely used is collaborative filtering. However, this method has lower accuracy because recommendations are limited to using quantitative information, such as user ratings or amount of use. To address this issue, many studies have been conducted to improve the accuracy of the recommendation system by using other types of information, in addition to quantitative information. Although conducting sentiment analysis using reviews is popular, previous studies show the limitation that results of sentiment analysis cannot be directly reflected in recommendation systems. Therefore, this study aims to quantify the sentiments presented in the reviews and reflect the results to the ratings; that is, this study proposes a new algorithm that quantifies the sentiments of user-written reviews and converts them into quantitative information, which can be directly reflected in recommendation systems. To achieve this, the user reviews, which are qualitative information, must first be quantified. Thus, in this study, sentiment scores are calculated through sentiment analysis by using a text mining technique. The data used herein are from movie reviews. A domain-specific sentiment dictionary was constructed, and then based on the dictionary, sentiment scores of the reviews were calculated. The collaborative filtering of this study, which reflected the sentiment scores of user reviews, was verified to demonstrate its higher accuracy than the collaborative filtering using the traditional method, which reflects only user rating data. To overcome the limitations of the previous studies that examined the sentiments of users based only on user rating data, the method proposed in this study successfully enhanced the accuracy of the recommendation system by precisely reflecting user opinions through quantified user reviews. Based on the findings of this study, the recommendation system accuracy is expected to improve further if additional analysis can be performed.

scholarly journals Sequential Recommendations on GitHub Repository

2021 ◽  
Vol 11 (4) ◽  
pp. 1585
Author(s):  
JaeWon Kim ◽  
JeongA Wi ◽  
YoungBin Kim
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Software Development ◽  
Online Community ◽  
Recommendation System ◽  
Development Platform ◽  
User Data ◽  
Active Research

The software development platform is an increasingly expanding industry. It is growing steadily due to the active research and sharing of artificial intelligence and deep learning. Further, predicting users’ propensity in this huge community and recommending a new repository is beneficial for researchers and users. Despite this, only a few researches have been done on the recommendation system of such platforms. In this study, we propose a method to model extensive user data of an online community with a deep learning-based recommendation system. This study shows that a new repository can be effectively recommended based on the accumulated big data from the user. Moreover, this study is the first study of the sequential recommendation system that provides a new dataset of a software development platform, which is as large as the prevailing datasets. The experiments show that the proposed dataset can be practiced in various recommendation tasks.

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