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Implicit Acquisition of User Personality for Augmenting Movie Recommendations
[chapter]
2015
Lecture Notes in Computer Science
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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]
2020
arXiv
pre-print
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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?
2012
Proceedings of the 13th International Conference on Electronic Commerce - ICEC '11
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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
2021
Journal of Organizational and End User Computing
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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
2010
Proceedings of the fourth ACM conference on Recommender systems - RecSys '10
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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
RESEARCH ON PERSONALIZED RECOMMENDATION ALGORITHM FUSING TIME AND LOCATION
2021
Converter
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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
2013
Journal of Intelligence and Information Systems
사용자 행동 기반의 사회적 관계를 결합한 사용자 협업적 여과 방법
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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
2018
International Journal of Trend in Scientific Research and Development
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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
2013
The Journal of Society for e-Business Studies
온라인 추천시스템에서 고객 사용의도를 위한 시스템 투명성과 피드백의 영향
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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
2022
Intelligent Automation and Soft Computing
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+ 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
2010
Proceedings of the fourth ACM conference on Recommender systems - RecSys '10
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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
HyPeRM: A HYBRID PERSONALITY-AWARE RECOMMENDER FOR MOVIE
2018
Malaysian Journal of Computer Science
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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
2015
MATEC Web of Conferences
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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]
2021
arXiv
pre-print
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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
2008
Proceedings of the 2008 ACM conference on Recommender systems - RecSys '08
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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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