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Predicting Latent Structured Intents from Shopping Queries

Chao-Yuan Wu, Amr Ahmed, Gowtham Ramani Kumar, Ritendra Datta
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
In this paper we study the problem of inferring the latent intent from unstructured queries and mapping them to structured attributes.  ...  In online shopping, users usually express their intent through search queries. However, these queries are often ambiguous.  ...  Contributions Our contributions are as follows: A new framework that predicts latent structured intents from shopping queries.  ... 
doi:10.1145/3038912.3052704 dblp:conf/www/WuAKD17 fatcat:mwmek7ltr5aj5eahp6wmqgs67u

Who Will Follow Your Shop? Exploiting Multiple Information Sources in Finding Followers [chapter]

Liang Wu, Alvin Chin, Guandong Xu, Liang Du, Xia Wang, Kangjian Meng, Yonggang Guo, Yuanchun Zhou
2013 Lecture Notes in Computer Science  
In this paper, we propose to predict the link relations between users and shops based on the following behavior.  ...  Then we adopt the latent factor model to calculate the similarity between users and shops, in which we use the multiple information sources to regularize the factorization.  ...  They designed and developed the service from scratch and allowed us to get access to the data and conduct this research work. Alvin Chin and Yuanchun Zhou are the corresponding authors.  ... 
doi:10.1007/978-3-642-37450-0_30 fatcat:yrwncnoq5fbr5hzicok56w265a

Subjective Search Intent Predictions using Customer Reviews

Adrian Boteanu, Emily Dutile, Adam Kiezun, Shay Artzi
2020 Proceedings of the 2020 Conference on Human Information Interaction and Retrieval  
We describe a method to predict latent query intents: we extract intents from product reviews on and, using behavioral purchase signals that associate queries with the reviewed products, train  ...  query classifiers that label queries with the intents extracted from reviews.  ...  In regard to predicting latent intents for queries, we evaluate the following hypotheses: (1) We can predict latent intents in queries by using entity extraction on customer review texts from reviews written  ... 
doi:10.1145/3343413.3377987 dblp:conf/chiir/BoteanuDKA20 fatcat:phx27j46vvdrthqt3rvnjrvgra

Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors

Manpreet Aneja Kaur, Flora Dilys Salim, Yongli Ren, Jeffrey Chan, Martin Tomko, Mark Sanderson
2020 ACM transactions on sensor networks  
We demonstrate the application of cyber-physical contextual similarity in two situations: user visit intent classification and future location prediction.  ...  This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators.  ...  Hence, we propose a system that uses structured information to find intent signals from user queries with respect to physical context, by extending the text of both queries and categories.  ... 
doi:10.1145/3393692 fatcat:3nka56skpffcjcpjnczcizlgqi

Sparse hidden-dynamics conditional random fields for user intent understanding

Yelong Shen, Jun Yan, Shuicheng Yan, Lei Ji, Ning Liu, Zheng Chen
2011 Proceedings of the 20th international conference on World wide web - WWW '11  
Understanding user intent from her sequential search behaviors, i.e. predicting the intent of each user query in a search session, is crucial for modern Web search engines.  ...  Extensive experiment results, on real user search sessions from a popular commercial search engine show that the proposed SHDCRF model significantly outperforms in terms of intent prediction results that  ...  Therefore, the coarse intent labels in previous provides little information for predicting the next user intent label.  ... 
doi:10.1145/1963405.1963411 dblp:conf/www/ShenYYJLC11 fatcat:uhxpxl72ifh4hfllhmpy6ecdpu

Learning a Hierarchical Embedding Model for Personalized Product Search

Qingyao Ai, Yongfeng Zhang, Keping Bi, Xu Chen, W. Bruce Croft
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
In this paper, we propose a hierarchical embedding model to learn semantic representations for entities (i.e. words, products, users and queries) from di erent levels with their associated language data  ...  representations for queries, products and users with a deep neural network; (3) each component of our network is designed as a generative model so that the whole structure is explainable and extendable  ...  For simplicity, we assume that user preferences are independent from query intents and build query-independent user models for personalized product search.  ... 
doi:10.1145/3077136.3080813 dblp:conf/sigir/AiZBCC17 fatcat:xppur3ulb5ck5pg6egffkc2yum

Attentive Long Short-Term Preference Modeling for Personalized Product Search [article]

Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Yinglong Wang, Jun Ma, Mohan Kankanhalli
2018 arXiv   pre-print
This unique design enables our model to capture users' current search intentions more accurately.  ...  They both affect users' current purchasing intentions. However, few research efforts have been dedicated to jointly model them for the personalized product search.  ...  [17] noticed it and learned the query intent representation collectively in both the query space and the structured entity space.  ... 
arXiv:1811.10155v1 fatcat:ser276xjc5ebnbhj3axpyvkaum

Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution [article]

Jacopo Tagliabue, Bingqing Yu
2020 arXiv   pre-print
We phrase the problem as a causal counterfactual inference, and contrast the approach with rule-based systems from industry settings and prediction models from the multi-touch attribution literature.  ...  , leveraging the latent space induced by prod2vec embeddings; we show how natural language queries can be effectively represented in the same space and how "search intervention" can be performed to assess  ...  When fully trained, our browsing model implicitly captures two important dimensions: first, how latent user intent shapes the unfolding of a shopping session; second, how site structure implicitly constraints  ... 
arXiv:2007.10087v1 fatcat:nzptlux2anh55cwuw7jkj7hzfu

Learning to extract cross-session search tasks

Hongning Wang, Yang Song, Ming-Wei Chang, Xiaodong He, Ryen W. White, Wei Chu
2013 Proceedings of the 22nd international conference on World Wide Web - WWW '13  
Search tasks, comprising a series of search queries serving the same information need, have recently been recognized as an accurate atomic unit for modeling user search intent.  ...  A semi-supervised clustering model is proposed based on the latent structural SVM framework, and a set of effective automatic annotation rules are proposed as weak supervision to release the burden of  ...  In Figure 3 , we illustrated the latent task structure inferred by our bestlink SVM from two different users' query logs.  ... 
doi:10.1145/2488388.2488507 dblp:conf/www/WangSCHWC13 fatcat:5ysw6q45knhzrk2yntot6ekx6i

Induced Over-Benefiting and Under-Benefiting on the Web: Inequity Effects on Feelings and Motivations with Implications for Consumption Behavior

Richard L. Oliver, Mikhael Shor, Simon T. Tidd
2004 Motivation and Emotion  
We embed equity treatments within a motivational structure to predict reactions to a quasi-shopping experience in which these methods operate in concert.  ...  Results showed strong negative effects on postexposure satisfaction, intention, and desire to complete the purchase in the empty coupon field group, and similar positive effects in the completed coupon  ...  Consistent with Hypothesis 2, expectations of satisfaction with the shopping experience were predictive of the combined (satisfaction/intention) latent variable at the time of checkout (β = .60, p < .01  ... 
doi:10.1023/b:moem.0000027279.32022.d0 fatcat:dua3yfkz5vholbldconwppachm

The Impact of Online Purchase Intentions Caused by Electronic Word of Mouth

Roskifzan Othman, Mohd Kamarul Irwan Abd Rahim
2019 International Journal of Academic Research in Business and Social Sciences  
Data was gathered through self-administered questionnaire from a sample of 385 Customers who were online users at numerous shopping center, shopping complex and supermarket located in the segmentation  ...  importance of online social interactions in order to encourage customer's intention to shop for through online.  ...  Conclusion Shopping online such as online retailers and the ability to develop understanding and the ability to predict intentions of online purchase customers will have an edge over their competitors  ... 
doi:10.6007/ijarbss/v9-i2/5670 fatcat:jpk726d2onhlpfuxgumqdiblpa

Contextual Intent Tracking for Personal Assistants

Yu Sun, Nicholas Jing Yuan, Yingzi Wang, Xing Xie, Kieran McDonald, Rui Zhang
2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16  
To solve the intent tracking problem, we propose the Kalman filter regularized PARAFAC2 (KP2) nowcasting model, which compactly represents the structure and co-movement of context and intent.  ...  Extensive experiments using real-world data sets from a commercial personal assistant show that the KP2 model significantly outperforms various methods, and provides inspiring implications for deploying  ...  This indicates that information intent is relevant to a higher dimensional latent space, which has a more complicated structure and the transition of latent factors is more flexible.  ... 
doi:10.1145/2939672.2939676 dblp:conf/kdd/SunYWXMZ16 fatcat:ynalqw4n4ngtxclencogfoevuq

User Intent Estimation from Access logs with Topic Model

Keisuke Uetsuji, Hidekazu Yanagimoto, Michifumi Yoshioka
2015 Procedia Computer Science  
Speaking concretely, we can predict customers' intents from the access logs since their internal intents affect their activities.  ...  In this paper, we propose a method to predict customer's intents from access logs in a real online shop. We adopt a Topic Tracking Model (TTM) to analyze the access logs.  ...  Nowadays LDA is used more widely to capture hidden structure from observation.  ... 
doi:10.1016/j.procs.2015.08.113 fatcat:pnhgy6i2yrebrmps2zaunmimc4

Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario [article]

Federico Bianchi, Jacopo Tagliabue, Bingqing Yu, Luca Bigon, Ciro Greco
2020 arXiv   pre-print
This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping intent from one website  ...  We then turn to the harder task of using learned embeddings across shops: if products from different shops live in the same vector space, user intent - as represented by regions in this space - can then  ...  At prediction time, we feed to the target shop model the aligned embeddings from the source Figure 5 : Landing pages can be customized in real-time by transferring intent from previous shops to the current  ... 
arXiv:2007.14906v1 fatcat:j3dd2v3rdrbx3btt6vixe4w2ou

The Effect of Perceived Risk in Online Shopping in Jordan: The Mediating Role of Intention and the Moderating Role of Experience

Muath Ayman Tarawneh, Abdul Malek Bin A. Tambi, Mutia Sobihah
2021 International Journal of Academic Research in Business and Social Sciences  
The findings indicated that intention partially mediated the relationship between perceived risk and actual online shopping behavior.  ...  Finally, to investigate the mediation effect of intention between perceived risk and actual online shopping behavior. Theory of planned behavior was adopted in this study.  ...  The next step is the assembly of these constructs into the structural model to perform Structural Equation Modeling (SEM). Constructs should be placed from left to right.  ... 
doi:10.6007/ijarbss/v11-i3/8490 fatcat:tm5nfp6r7veztnwxbpbs5mmkam
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