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Implicit Acquisition of User Personality for Augmenting Movie Recommendations [chapter]

Wen Wu, Li Chen
2015 Lecture Notes in Computer Science  
In this paper, we focus on deriving users' personality from their implicit behavior in movie domain and hence enabling the generation of recommendations without involving users' efforts.  ...  outperforms related methods in terms of both rating prediction and ranking accuracy.  ...  We thank grants ECS/HKBU211912 and NSFC/61272365. References  ... 
doi:10.1007/978-3-319-20267-9_25 fatcat:rkzgki2bfjcvfb6owzbo46mikm

Personalized Multimedia Item and Key Frame Recommendation [article]

Le Wu, Lei Chen, Yonghui Yang, Richang Hong, Yong Ge, Xing Xie, Meng Wang
2020 arXiv   pre-print
We further design a model to discern both the collaborative and visual dimensions of users, and model how users make decisive item preferences from these two spaces.  ...  ., related images of the movie), personalized key frame recommendation is necessary in these applications to attract users' unique visual preferences.  ...  Science Foundation(Grant No. 1708085QF155), and the Fundamental Research Funds for the Central Universities(Grant No.  ... 
arXiv:1906.00246v2 fatcat:v5jxwslwnnfjxn6cxc7qj46pa4

Who are the most influential users in a recommender system?

Mohammad Amin Morid, Mehdi Shajari, Alireza Hashemi Golpayegani
2012 Proceedings of the 13th International Conference on Electronic Commerce - ICEC '11  
In order to recommend a product to a user and predict her preference, CF utilizes product evaluation ratings of the like-minded users.  ...  This paper attempts to model and analyze the behavior of these users by employing data mining techniques.  ...  The correlation r il , specifies the impact of the user l's preferences on the user i's predicted preference which results in a more similar user i causes a higher impact to the predicted preference of  ... 
doi:10.1145/2378104.2378123 fatcat:7d3p7hueljcu7cwmuj2w57k5ay

Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System

Dan Yang, Zheng Tie Nie, Fajun Yang
2021 Journal of Organizational and End User Computing  
And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences.  ...  Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed.  ...  Time-Aware Item Rating Prediction We assume that the more recent data of N(u) has greater impact on item rating prediction for the target user u, i.e, user preferences are time-aware.  ... 
doi:10.4018/joeuc.20210501.oa2 fatcat:griyrls44bh4tfnoosvemekg3q

Increasing consumers' understanding of recommender results

Paul Marx, Thorsten Hennig-Thurau, André Marchand
2010 Proceedings of the fourth ACM conference on Recommender systems - RecSys '10  
The method models the users' ratings as a function of their utility part-worths for those item attributes which influence the users' evaluation behavior, with part-worth being estimated through a set of  ...  Recommender systems are intended to assist consumers by making choices from a large scope of items.  ...  ACKNOWLEDGMENTS The authors thank Tobias Bauckhage for providing the data for our tests, Denis Rechkin for many fruitful discussions, and three anonymous reviewers for their helpful comments.  ... 
doi:10.1145/1864708.1864771 dblp:conf/recsys/MarxHM10 fatcat:okxat4x23zeu3hnidimaqudgey


Zhongyong Fan, Yongqian Zhao, Yongkang Wang, Zhijun Zhang
2021 Converter  
In order to relieve problems existing in commodity selection by users of different preferences from different regions, personalized recommendation based on location information has emerged.  ...  To solve the above problem, geographic location and time factor of users are effectively combined in this paper, and a personalized recommendation algorithm TLPR combining time and location information  ...  has totally different user preference from Toronto.  ... 
doi:10.17762/converter.130 fatcat:alpufpfpmnbapgxkplrl2ymwle

Incorporating Social Relationship discovered from User's Behavior into Collaborative Filtering
사용자 행동 기반의 사회적 관계를 결합한 사용자 협업적 여과 방법

Setha Thay, Inay Ha, Geun-Sik Jo
2013 Journal of Intelligence and Information Systems  
Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items.  ...  CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences.  ...  get rating information on movies of each user and also get permission from user to obtain social data from their personal profile by allowing each user to authenticate with our website before they can  ... 
doi:10.13088/jiis.2013.19.2.001 fatcat:5wjkxrzfvrgfzkxo6h6cksyk5q

Survey on Kernel Optimization based Enhanced Preference Learning for Online Movie Recommendation

Sreelekshmi. B, Vidya. N
2018 International Journal of Trend in Scientific Research and Development  
Also use SVM (Support Vector Machine) to predict user preferences using the model learning method. SVM is a classification method which is used to classify movies.  ...  The proposed system is a new online social movie recommendation framework from the view point of online graph regularized user preference learning (OGRPL), which incorporates the user-item collaborative  ...  Content-based prediction is used to train each user rating vector in the user rating matrix and convert it to a pseudo-rating matrix, combining the actual rating with the prediction rating.  ... 
doi:10.31142/ijtsrd11305 fatcat:7g3o5dgzejbcvitacutm5lcm3y

Influences of Transparency and Feedback on Customer Intention to Reuse Online Recommender Systems
온라인 추천시스템에서 고객 사용의도를 위한 시스템 투명성과 피드백의 영향

Januel L. Hebrado, Hong Joo Lee, Jaewon Choi
2013 The Journal of Society for e-Business Studies  
RSs aid users in filtering products and decisions on matters relating to personal taste.  ...  The problem of choosing the right product that will best fit a consumer's taste and preferences extends to the field of electronic commerce.  ...  Subjects rated their movie preferences and evaluated the recommended movies. In the first stage, we showed 20 movies to obtain user preferences.  ... 
doi:10.7838/jsebs.2013.18.2.279 fatcat:74d7obgiq5covmcliggh4uypoe

Movie Recommendation Algorithm Based on Ensemble Learning

Wei Fang, Yu Sha, Meihan Qi, Victor S. Sheng
2022 Intelligent Automation and Soft Computing  
+ movie KNN, and the recommendation of user KNN + singular value decomposition.  ...  of the website to deviate from the need of users, and affect the experience of using.  ...  Acknowledgement: The author would like to thank the researchers in the field of recommendation algorithm and other related fields. This paper cites the research literature of several scholars.  ... 
doi:10.32604/iasc.2022.027067 fatcat:jqkaj3sferda5i2c3q5arzihua

Design and user issues in personality-based recommender systems

Rong Hu
2010 Proceedings of the fourth ACM conference on Recommender systems - RecSys '10  
Recommender systems have emerged as an intelligent information filtering tool to help users effectively identify information items of interest from an overwhelming set of choices and provide personalized  ...  The overall goal is to develop an efficient personality-based recommender system and to arrive at a series of design guidelines from the perspective of human computer interaction.  ...  dimensions are in proportion to the predicted preference values.  ... 
doi:10.1145/1864708.1864790 dblp:conf/recsys/Hu10 fatcat:5awgvbavs5btlniojc25zwye6u


Vimala Balakrishnan, Hossein Arabi
2018 Malaysian Journal of Computer Science  
The popular Big Five personality trait measurement scale was used to gauge users' personalities.  ...  The study shows that user recommendations can be further enhanced when their personality traits are taken into consideration, and thus their overall search experience can be improved as well.  ...  Movies rating were taken as average of all user rating; where recommended movies with no rating, the chi square value of the particular movie user will increase.  ... 
doi:10.22452/mjcs.vol31no1.4 fatcat:josdxogcq5ehfmlj7csthhuxgy

User Adoption Tendency Modeling for Social Contextual Recommendation

Zhisheng Wang, Qi Li, Ye Liu, Jian Yin, J.Y. Li, T.Y. Liu, T. Deng, M. Tian
2015 MATEC Web of Conferences  
Assuming that user behaviors are affected by the user personal preference (the user preference factor) and external environment conditions (the social contextual influence), our model generates the recommendation  ...  Where R ij is the user i's rating score on item j and B pq is the ratio of user p's adoptions at feature q (For example, if 30% of adopted movies are horror movies, then the sensitive value is 0.3).  ... 
doi:10.1051/matecconf/20152201034 fatcat:fmjl723ua5dh7licqnpkidoply

Sequential Movie Genre Prediction using Average Transition Probability with Clustering [article]

Jihyeon Kim, Jinkyung Kim, Jaeyoung Choi
2021 arXiv   pre-print
In recent movie recommendations, predicting the user's sequential behavior and suggesting the next movie to watch is one of the most important issues.  ...  In particular, in this paper, users with similar genre preferences are organized into clusters to recommend genres, and in clusters that do not have relatively specific tendencies, genre prediction is  ...  However, it is not an easy task to learn the personalized sequential behavior from collaborative data since long and short term dynamics of the users have to be carefully considered for both personalization  ... 
arXiv:2111.02740v1 fatcat:kfl4hlqri5hwna53nx4ogs6xiq

Crafting the initial user experience to achieve community goals

Sara Drenner, Shilad Sen, Loren Terveen
2008 Proceedings of the 2008 ACM conference on Recommender systems - RecSys '08  
Recommender systems try to address the "new user problem" by quickly and painlessly learning user preferences so that users can begin receiving recommendations as soon as possible.  ...  We take an expanded perspective on the new user experience, seeing it as an opportunity to elicit valuable contributions to the community and shape subsequent user behavior.  ...  Over 90% of tags in MovieLens have been rated. Tag ratings are independent from movie ratings, and currently have no impact on movie recommendations and predictions.  ... 
doi:10.1145/1454008.1454039 dblp:conf/recsys/DrennerST08 fatcat:2zyooi5yhngydhnyeohtgki5ru
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