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Recommender Systems as a Mobile Marketing Service

Donald J. Kridel, Daniel R. Dolk, David Castillo
2013 Journal of Service Science and Management  
We discuss and compare three versions of a CF-based recommender system based upon customer purchase history, customer browsing history, and user segments respectively.  ...  Collaborative filtering (CF) is based upon the premise that users who have purchased a particular product will have similar preferences to other users who also purchased the product.  ...  Our CF-driven RS combines purchase behavior, previous browsing behavior, and user segments into a hybrid system which can be used to augment a part of a firm's current CRM process, especially with regard  ... 
doi:10.4236/jssm.2013.65a004 fatcat:healoxv6g5fabbho6pxeorjn2m

Engagement, Search Goals and Conversion - The Different M-Commerce Path to Conversion

Anat Goldstein, Orit Raphaeli, Shachar Reichman
2016 International Conference on Information Systems  
In this research, using detailed event log-files of an online jewelry retailer, we analyze user engagement and navigation behaviors on both platforms, model search goals and their effect on purchase decisions  ...  , and develop a conversion prediction model.  ...  For example, Montgomery et al. (2004) show that users' browsing paths may reflect users' goals and help predict whether the session will end with a purchase.  ... 
dblp:conf/icis/GoldsteinRR16 fatcat:p4t4qhsn4fewxaz5t5bxhirbtq

TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning [article]

Jingxing Jiang, Zhubin Wang, Fei Fang, Binqiang Zhao
2020 arXiv   pre-print
We creatively use the GRU structure and total probability formula as the loss function to model the users' whole online purchase process.  ...  At present, the proposed user intent prediction model has been widely used for the coupon allocation, advertisement and recommendation on Taobao platform, which greatly improve the user experience and  ...  For example, for users who browse a lot but purchase little, their browsing probability are very high and purchase intention are quite low, which indicates that they have purchase demand but may not be  ... 
arXiv:2008.02122v1 fatcat:d6x7gywndveb7eh3ddhn2mtx5e

When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity [article]

Tong Chen, Lin Wu, Yang Wang, Jun Zhang, Hongxu Chen, Xue Li
2017 arXiv   pre-print
history and asynchronous event sequences in the context of inter-user behavioral mutual infectivity.  ...  Predicting fine-grained interests of users with temporal behavior is important to personalization and information filtering applications.  ...  of leveraging the influence from history for finegrained user interest prediction.  ... 
arXiv:1710.05135v2 fatcat:rf5ndhkhifhxzdydjtyem3bjau

Prediction of Purchase Intention among E-Commerce Platform Users Based on Big Data Analysis

Qian Guo, Chun Yang, Shaoqing Tian
2020 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
To realize precision marketing, the ECPs must mine out the effective information from the massive data, and predict the purchase intention of their users.  ...  Therefore, this paper attempts to design an effective prediction model of purchase intention among ECP users.  ...  [2] predicted the purchase intention of consumers by analyzing their behavior data, and examined the features of consumers who intend to make repeat purchase.  ... 
doi:10.18280/ria.340113 fatcat:7m5mv2nu7jalvaxnqsqchnyllu

Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data [article]

Jiarui Jin, Xianyu Chen, Weinan Zhang, Junjie Huang, Ziming Feng, Yong Yu
2022 arXiv   pre-print
The personalized recommendation is an essential part of modern e-commerce, where user's demands are not only conditioned by their profile but also by their recent browsing behaviors as well as periodical  ...  purchases made some time ago.  ...  In validation set, we use 1-st to (𝑇 − 2)-th user records to predict (𝑇 − 1)-th user behavior, and in the test set, we use 1-st to (𝑇 − 1)-th behaviors to predict 𝑇 -th behavior.  ... 
arXiv:2202.03097v1 fatcat:ao5fdoeqnfcfjoag7ufm7pvaza

Recommending Web Advertisements based on Long-Short Term User Interest

Panote Siriaraya, Yuriko Yamaguchi, Mimpei Morishita, Yoichi Inagaki, Reyn Y. Nakamoto, Jianwei Zhang, Shinsuke Nakajima
2019 International Conference on Intelligent User Interfaces  
section of user browsing history was used to represent the overall interests of users.  ...  As part of this work, we describe an approach which could be used to help predict the latent interest of users by analyzing their long and short term interests based on a large dataset of user web browsing  ...  Logistic regression was used to predict whether a user might be interested in a given website (which the user has never visited before) by analyzing their web browsing history in comparison to the web  ... 
dblp:conf/iui/SiriarayaYMIN0N19 fatcat:qa6ws4nojncmfodp73hmideype

A Web Usage Mining for Modeling Buying Behavior at a Web Store using Network Analysis

Miseon Lim, Hyunsoo Byun, Jinhwa Kim
2015 Indian Journal of Science and Technology  
Online users visit lots of sites and their activities include information acquisition and browsing. The history of these activities can be used to construct a relationship network among web sites.  ...  Thirdly, there can be more meaningful predictors to predict users purchase behavior except variables used in this study.  ... 
doi:10.17485/ijst/2015/v8i25/85359 fatcat:h6knthqhgja7rndfxxpkexbk7e

How Users Perceive and Appraise Personalized Recommendations [chapter]

Nicolas Jones, Pearl Pu, Li Chen
2009 Lecture Notes in Computer Science  
However recent recommender systems have gone as far as using implicit feedback indicators to understand users' interests.  ...  In this paper we report an in-depth user study comparing Amazon's implicit book recommender with a baseline model of explicit search and browse.  ...  In behavior based RS, a user's purchase history or his reading time on a page can be used to infer interests and preferences.  ... 
doi:10.1007/978-3-642-02247-0_53 fatcat:r5og6o3ahze63ochxhbcxlzhaq

A method for discovering clusters of e-commerce interest patterns using click-stream data

Qiang Su, Lu Chen
2015 Electronic Commerce Research and Applications  
Generally speaking, user's browsing behaviors (when looking at websites) represent a comprehensive reflection of their interests.  ...  The browsing behavior of a number of consumers -including their visiting sequence, frequency and time spent on each category -are mined via the click-stream data recorded on an e-commerce website.  ...  Meanwhile, many of the studies used click-stream data to explore users' behavioral characteristics, including users' browsing behavior (Moe and Fader 2004, Montgomery et al. 2004) , users' responses to  ... 
doi:10.1016/j.elerap.2014.10.002 fatcat:3oq7kqw7ynb43ao4xqgaow7mom

Analysis of Customer Behavior Using Web Usage Mining

2020 Information and Knowledge Management  
With the assistance of this information, user behavior can be predicted very easily.  ...  This paper attempts to figure out the concealed data and establish user behavior online by exploiting the online as a source for data collection.  ...  Web Mining explores client's behavioral pattern and predicts their future interaction. These predictions can be used to enhance the website and to recommend products based on clients' preference.  ... 
doi:10.7176/ikm/10-6-02 fatcat:gydxi2gx7vhopcyeyuszibq5bi

A New Latent Class Model for Analysis of Purchasing and Browsing Histories on EC Sites

Masayuki Goto, Kenta Mikawa, Shigeichi Hirasawa, Manabu Kobayashi, Tota Suko, Shunsuke Horii
2015 Industrial Engineering & Management Systems  
This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters.  ...  The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products; their access logs, including purchase records and browsing histories  ...  The authors also thank the area editor and reviewers for their constructive feedback. A part of this study was supported by JSPS KAKENHI Grant Numbers 26282090 and 26560167.  ... 
doi:10.7232/iems.2015.14.4.335 fatcat:k7mpn3brivczlkvhbyxaap5jdm

A Comparative Study of Different Data Mining Algorithms with Different Oversampling Techniques in Predicting Online Shopper Behavior

Ruba Obiedat
2020 International Journal of Advanced Trends in Computer Science and Engineering  
Therefore, this paper will study the behavior of online shoppers to predict whether they will buy a product or not.  ...  Nowadays buying over the internet has become very popular among online users.  ...  Studies prove that the users' navigational behavior presents the main source of information for their preference. Studying users' navigational history can help in predicting their next behavior.  ... 
doi:10.30534/ijatcse/2020/164932020 fatcat:5o3pnqiksnfz5bsnocwf2z756q

E-Commerce Personalization in Africa: A Comparative Analysis of Jumia and Konga

Makuochi Nkwo, Rita Orji, Joshua C. Nwokeji, Chinenye Ndulue
2018 International Conference on Persuasive Technology  
We also compared how personalized experiences are uniquely provided to each customer using the traces of user's purchase history, browsing history, user preferences, on-site behaviour, and personal data  ...  In this paper, we present the results of an analysis of Jumia and Konga, (the two biggest E-Commerce stores in Africa) to highlight personalization techniques they implemented using framework for E-Commerce  ...  They analyze user's previous browsing behaviors and recommend products that they predict the user might want.  ... 
dblp:conf/persuasive/NkwoONN18 fatcat:bzlpdlezxjcupeylt3jiihpsaa

Recommendations Based on Purchase Patterns

Haiyun Lu
2014 International Journal of Machine Learning and Computing  
We propose an approach of recommendation based on purchase patterns. The purchase history of users is analyzed to find their purchase patterns related to user behavior.  ...  These patterns are then used to predict the category of next possible purchase in a particular location. The proposed approach is experimented on real transaction data.  ...  The purchase history of users is analyzed to find their purchase patterns related to user behavior.  ... 
doi:10.7763/ijmlc.2014.v6.462 fatcat:zea2etwgxfdp5ogzmny7fbktli
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