Predictive Approach for User Long-Term Needs in Content-Based Image Suggestion

In this paper, we formalize content-based image suggestion (CBIS) as a Bayesian prediction problem. In CBIS, users provide the rating of images according to both their long-term needs and the contextual situation, such as time and place, to which they belong. Therefore, a CBIS model is defined to fit the distribution of the data in order to predict relevant images for a given user. Generally, CBIS becomes challenging when only a small amount of data is available such as in the case of “new users” and “new images.” The Bayesian predictive approach is an effective solution to such a problem. In addition, this approach offers efficient means to select highly rated and diversified suggestions in conformance with theories in consumer psychology. Experiments on a real data set show the merits of our approach in terms of image suggestion accuracy and efficiency.

[1]  Mi Zhang,et al.  Avoiding monotony: improving the diversity of recommendation lists , 2008, RecSys '08.

[2]  Michael I. Jordan,et al.  On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.

[3]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[4]  Philip S. Yu.  Editorial: State of the Transactions , 2004, IEEE Trans. Knowl. Data Eng..

[5]  Djemel Ziou,et al.  Image Retrieval from the World Wide Web: Issues, Techniques, and Systems , 2004, CSUR.

[6]  Arnaud De Bruyn,et al.  Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids , 2008, Mark. Sci..

[7]  Tommy W. S. Chow,et al.  Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach , 2011, IEEE Transactions on Neural Networks.

[8]  Thomas Hofmann,et al.  Unifying collaborative and content-based filtering , 2004, ICML.

[9]  Wei-Ying Ma,et al.  Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes , 2002, UAI.

[10]  Hans-Peter Kriegel,et al.  Ieee Transactions on Knowledge and Data Engineering Probabilistic Memory-based Collaborative Filtering , 2022 .

[11]  Mariëlle E. H. Creusen,et al.  The Different Roles of Product Appearance in Consumer Choice , 2005 .

[12]  Samuel Kaski,et al.  Two-Way Latent Grouping Model for User Preference Prediction , 2005, UAI.

[13]  Benjamin M. Marlin,et al.  Modeling User Rating Profiles For Collaborative Filtering , 2003, NIPS.

[14]  Giuseppe De Nicolao,et al.  Client–Server Multitask Learning From Distributed Datasets , 2008, IEEE Transactions on Neural Networks.

[15]  D. Madigan,et al.  Correction to: ``Bayesian model averaging: a tutorial'' [Statist. Sci. 14 (1999), no. 4, 382--417; MR 2001a:62033] , 2000 .

[16]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[17]  Sylvain Gelly,et al.  Exploration exploitation in Go: UCT for Monte-Carlo Go , 2006, NIPS 2006.

[18]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[19]  Nizar Bouguila,et al.  A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Peter Grünwald,et al.  Invited review of the book Statistical and Inductive Inference by Minimum Message Length , 2006 .

[21]  A. Raftery,et al.  Estimating Bayes Factors via Posterior Simulation with the Laplace—Metropolis Estimator , 1997 .

[22]  B. Kahn.  Consumer variety-seeking among goods and services: An integrative review , 1995 .

[23]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[24]  Djemel Ziou,et al.  A Graphical Model for Context-Aware Visual Content Recommendation , 2008, IEEE Transactions on Multimedia.

[25]  Anil K. Jain,et al.  Simultaneous feature selection and clustering using mixture models , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Yi Zhang,et al.  Novelty and redundancy detection in adaptive filtering , 2002, SIGIR '02.

[27]  C. S. Wallace,et al.  Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics) , 2005 .

[28]  Grace I. Kunz,et al.  Individual differences, motivations, and willingness to use a mass customization option for fashion products , 2004 .

[29]  R. Belk.  Situational Variables and Consumer Behavior , 1975 .

[30]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[31]  Daniel A. Levinthal,et al.  Exploration and Exploitation in Organizational Learning , 2007 .

[32]  Luo Si,et al.  Flexible Mixture Model for Collaborative Filtering , 2003, ICML.

[33]  Ricardo de Borobia Pires Gonçalves,et al.  Consumer Behavior: Product Characteristics and Quality Perception , 2008 .

[34]  Samuel Kaski,et al.  Latent grouping models for user preference prediction , 2008, Machine Learning.

[35]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

[36]  Nizar Bouguila,et al.  Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data , 2007, NIPS.

[37]  Paul Messaris,et al.  Visual Persuasion: The Role of Images in Advertising , 1996 .

[38]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[39]  E. Hirschman.  Innovativeness, Novelty Seeking, and Consumer Creativity , 1980 .

[40]  Nizar Bouguila,et al.  Advanced Off-Line Statistical Modeling of Eye and Non-Eye Patterns , 2008 .

[41]  Francisco Escolano,et al.  Entropy-Based Incremental Variational Bayes Learning of Gaussian Mixtures , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[42]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[43]  James Ze Wang,et al.  Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach , 2003, IEEE Trans. Pattern Anal. Mach. Intell..