Personal Tastes vs. Fashion Trends: Predicting Ratings Based on Visual Appearances and Reviews

People have their own tastes on visual appearances of products from various categories. For many of them, the tastes are affected by the current fashion trend. Studying visual appearances and fashion trend makes us understand the composition of users’ preferences and their purchase choices. However, since the fashion is changing over time, it is complex to model time-aware and non-time-aware variables simultaneously. In this paper, we present VIsually-aware Temporal rAting modeL with topics using review text to help mine visual dynamics and non-visual features for rating prediction task. Understanding the reviews will help the Recommender Systems (RSs) know whether a user is attracted by the appearance of an item, and which aspect of an item’s appearance contributes most to its ratings. To achieve this, we incorporate the visual information into the rating predicting function and introduce a topic model that can automatically classify words in an item’s reviews into non-visual words that explain the coherent feature, and visual words that are associated with its visual appearances in each time period, respectively. We run experiments on eleven real-world public datasets and the results show that our model performs better on predicting ratings than many of the state-of-the-art RSs, such as PMF, timeSVD++, HFT, JMARS, ETDR, and TVBPR+.

[1]  Jianguo Wang,et al.  Sherlock: Sparse Hierarchical Embeddings for Visually-Aware One-Class Collaborative Filtering , 2016, IJCAI.

[2]  Keqiu Li,et al.  Recommendation in a Changing World , 2017, ACM Trans. Web.

[3]  Jimeng Sun,et al.  Temporal recommendation on graphs via long- and short-term preference fusion , 2010, KDD.

[4]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[6]  Serge J. Belongie,et al.  Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Jure Leskovec,et al.  From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews , 2013, WWW.

[8]  Richard S. Zemel,et al.  Collaborative Filtering and the Missing at Random Assumption , 2007, UAI.

[9]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[10]  Xu Ling,et al.  Topic sentiment mixture: modeling facets and opinions in weblogs , 2007, WWW '07.

[11]  Bing Liu,et al.  Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews , 2017, KDD.

[12]  Alexander Tuzhilin,et al.  The effect of context-aware recommendations on customer purchasing behavior and trust , 2011, RecSys '11.

[13]  Lars Schmidt-Thieme,et al.  Multi-relational matrix factorization using bayesian personalized ranking for social network data , 2012, WSDM '12.

[14]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[15]  Robinson Piramuthu,et al.  Large scale visual recommendations from street fashion images , 2014, KDD.

[16]  J. Nocedal.  Updating Quasi-Newton Matrices With Limited Storage , 1980 .

[17]  Shujian Huang,et al.  A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews , 2015, IJCAI.

[18]  Yue Lu,et al.  Latent aspect rating analysis without aspect keyword supervision , 2011, KDD.

[19]  Aidong Zhang,et al.  Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts , 2017, KDD.

[20]  Julian J. McAuley,et al.  VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback , 2015, AAAI.

[21]  Hongfei Yan,et al.  Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid , 2010, EMNLP.

[22]  Alexander J. Smola,et al.  Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) , 2014, KDD.

[23]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

[24]  Michael R. Lyu,et al.  Ratings meet reviews, a combined approach to recommend , 2014, RecSys '14.

[25]  Chen Fang,et al.  Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation , 2016, RecSys.

[26]  Tao Chen,et al.  TriRank: Review-aware Explainable Recommendation by Modeling Aspects , 2015, CIKM.

[27]  Julian J. McAuley,et al.  Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering , 2016, WWW.