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Comparing click-through data to purchase decisions for retrieval evaluation

Katja Hofmann, Bouke Huurnink, Marc Bron, Maarten de Rijke
2010 Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10  
We compare assessments derived from click-through data to another source of implicit feedback that we assume to be highly indicative of relevance: purchase decisions.  ...  Evaluating retrieval runs based on a log of an audiovisual archive, we find agreement between system rankings and purchase decisions to be surprisingly high.  ...  We compare the use of click data for system evaluation to a related form of implicit relevance judgment -purchase decisions.  ... 
doi:10.1145/1835449.1835603 dblp:conf/sigir/HofmannHBR10 fatcat:6bwvw2vj5fgxfmmmuhqrn4wjta

Leverage Implicit Feedback for Context-aware Product Search [article]

Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, W. Bruce Croft
2020 arXiv   pre-print
In contrast to web search, the retrieved results in product search not only need to be relevant but also should satisfy customers' preferences in order to elicit purchases.  ...  Hence, in this paper, we leverage clicks within a query session, as implicit feedback, to represent users' hidden intents, which further act as the basis for re-ranking subsequent result pages for the  ...  ACKNOWLEDGMENTS This work was supported in part by the Center for Intelligent Information Retrieval and in part by NSF IIS-1715095.  ... 
arXiv:1909.02065v2 fatcat:o2ceae6g5bav3ioddwjbjgxsgy

Page-level Optimization of e-Commerce Item Recommendations [article]

Chieh Lo, Hongliang Yu, Xin Yin, Krutika Shetty, Changchen He, Kathy Hu, Justin Platz, Adam Ilardi, Sriganesh Madhvanath
2021 arXiv   pre-print
Through extensive offline experimentation and online A/B testing, we show that our proposed system achieves significantly higher click-through and conversion rates compared to other existing methods.  ...  In our online A/B test, our framework improved click-through rate by 2.48% and purchase-through rate by 7.34% over a static configuration.  ...  Unbiased offline evaluation In addition to the above-mentioned metrics, we also estimate the CTR (click through rate) and PTR (purchase through rate) to evaluate relevance and conversion, respectively.  ... 
arXiv:2108.05891v1 fatcat:pthcwahr5vfsbflvanmtwy6rxy

When relevance is not Enough: Promoting Visual Attractiveness for Fashion E-commerce [article]

Wei Di, Anurag Bhardwaj, Vignesh Jagadeesh, Robinson Piramuthu, Elizabeth Churchill
2014 arXiv   pre-print
We propose a Fisher noncentral hypergeometric distribution based user choice model to quantitatively evaluate user's preference.  ...  Further, we investigate the potentials to leverage visual impact for a better search that caters to user's preference.  ...  Compared to unclicked items, clicked items show higher conversion rate, which is expected as user shows interest in the item through clicking, which leads to higher chance of purchase.  ... 
arXiv:1406.3561v1 fatcat:4tc6qy2gvbd73gbfjo5ecaqi7a

Generating Consumer Insights from Big Data Clickstream Information and the Link with Transaction-Related Shopping Behavior

Daniel Schellong, Jan Kemper, Malte Brettel
2017 European Conference on Information Systems  
Adding to purchase decision-making theory we propose that the use of off-site clickstream data-the sequence of consumers' advertising channel clicks to a firm's website-can significantly enhance the understanding  ...  To run our consumer data analytics we use a unique and extensive dataset from a large European apparel company with over 80 million clicks covering 11 online advertising channels.  ...  purchase or non-purchase decision (see Data section for more detailed information on the definition off-site clickstream).  ... 
dblp:conf/ecis/SchellongKB17 fatcat:vg2t7znkvnd5rcnsgnf2pqulj4

ARShopping: In-Store Shopping Decision Support Through Augmented Reality and Immersive Visualization [article]

Bingjie Xu, Shunan Guo, Eunyee Koh, Jane Hoffswell, Ryan Rossi, Fan Du
2022 arXiv   pre-print
This prototype uses augmented reality (AR) to identify products and display detailed information to help consumers make purchasing decisions that fulfill their needs while decreasing the decision-making  ...  However, making purchasing decisions in physical stores can be challenging due to a large number of similar alternatives and limited accessibility of the relevant product information (e.g., features, ratings  ...  ., MyGrocer [16] ) to retrieve product information at the point of purchase. Shoppers may also retrieve data by scanning product barcodes or QR-codes with their phone camera [15, 30] .  ... 
arXiv:2207.07643v1 fatcat:nr4daxsu7nagnhubnyhhz6v45m

A Content-based Recommender System for E-commerce Offers and Coupons

Yandi Xia, Giuseppe Di Fabbrizio, Shikhar Vaibhav, Ankur Datta
2017 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval  
When compared to the popularity-based baseline, our content-based recommender system improves F-measures from 0.21 to 0.85 and increases the estimated click-through rate from 1.20% to 7.80%.  ...  High-quality online discounts are selected and delivered through these two means by applying a manual process that involves a team of experts who are responsible for evaluating recency, product popularity  ...  The authors would also like to thank the anonymous reviewers for their helpful advice and Yiu-Chang Lin for presenting our work at the workshop.  ... 
dblp:conf/sigir/XiaFVD17 fatcat:o2tzchwrl5d4nabvrktnl4yvd4

A novel approach to dynamic profiling of e-customers considering click stream data and online reviews

Houda Zaim, Adil Haddi, Mohammed Ramdani
2019 International Journal of Electrical and Computer Engineering (IJECE)  
The challenge does not only lay in analyzing how customer's classifier model change and when it does so but also to adapt it to the customer's click stream data using a new decision tree generation algorithm  ...  Experiments show that this work performed well in identifying relevant customer's stream data to judge the chinese e-commerce website "Tmall".  ...  User profiles can also be created through multiresolution clustering designed for smart metering data [5] .  ... 
doi:10.11591/ijece.v9i1.pp602-612 fatcat:35vghcg6q5aafjvlvj45qt376m

Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search

Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, Filip Radlinski, Geri Gay
2007 ACM Transactions on Information Systems  
First, our study provides detailed insight into the users' decision-making process through the use of eyetracking. Second, we evaluate relative preference signals derived from user behavior.  ...  Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased.  ...  It states that consumer behavior (i.e. purchasing decision from a set of options) can be used to reveal the (typically unobservable) utility function that governs the consumer's decision process.  ... 
doi:10.1145/1229179.1229181 fatcat:nlxsb2baprf5xgfd67bn4qktru

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  
However, the number of actual buyers is still very low compared to the number of total visitors of these sites.  ...  The study first compares several classification algorithms against each other, then tries to enhance the results using different oversampling techniques.  ...  One of the main indicators for the online shoppers' behavior and purchasing intention is the click-stream data.  ... 
doi:10.30534/ijatcse/2020/164932020 fatcat:5o3pnqiksnfz5bsnocwf2z756q

Behavioural Analytics using Process Mining in On-line Advertising

Maria Diapouli, Stelios Kapetanakis, Miltos Petridis, Roger Evans
2017 International Conference on Case-Based Reasoning  
It is based primarily on analysing web user behavioural data with the usage of machine learning techniques with the aim to optimise web advertising.  ...  Being able to identify "unknown" and "first time seen" customers is of high importance in online advertising since a successful guess could identify "possible prospects" who would be more likely to purchase  ...  compared to the other baselines (88.25% for C&RT, 89.34% for CHAID and 89.14% for 3NN).  ... 
dblp:conf/iccbr/DiapouliKPE17 fatcat:bwb6iz7senf6zo2q6lgho2bnqq

Competitive Analytics of Multi-channel Advertising and Consumer Inertia

Yiyi Li, Ying Xie, Eric Zheng
2015 Americas Conference on Information Systems  
We propose an integrated individual-level choice model that considers three stages of a consumer's purchase funnel -awareness, alternative evaluation and purchase -across all competitors to analyze the  ...  effects of touches on (1) consumers' choice of entry site, (2) their subsequent search decisions about other websites in the awareness set, and (3) their subsequent purchases at one of the searched websites  ...  (2.12) We observe ad click-throughs but not ad exposures, and so we aggregate the number of click-throughs from each ad channel to represent the information stock for each website.  ... 
dblp:conf/amcis/LiXZ15 fatcat:c6i2oie6zbcslau6um6cmdkrjm

Integrating Collaborative Filtering and Matching-based Search for Product Recommendations

Noraswaliza Abdullah, Yue Xu, Shlomo Geva
2013 Journal of Theoretical and Applied Electronic Commerce Research  
These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products  ...  In this article, a simple user profiling approach is proposed to generate user's preferences to product attributes (i.e., user profiles) based on user product click stream data.  ...  Click streams data is a kind of search log that could be collected by the search engine implicitly without user extra effort. Click stream data shows the path a user takes through a website.  ... 
doi:10.4067/s0718-18762013000200004 fatcat:jtgovqkkgrft7dr4hok4xih7l4

A Theory-Based Approach for a Modular System of Interactive Decision Aids

Jella Pfeiffer, Felix Vogel, Sven Stumpf, Charlotte Kiltz
2010 Americas Conference on Information Systems  
The contribution of this paper is to (1) retrieve guidelines for designing such tools from both literature on decision behavior research and information systems, and (2) build a prototype following these  ...  Therefore, our goal is to prevent consumers from information overload by supporting the cumbersome process of comparing and evaluating products.  ...  A Modular System of Interactive Decision Aids Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, August 12-15, 2010  ... 
dblp:conf/amcis/PfeifferVSK10 fatcat:4jayv5jxbfhx7mejwyxdgy3yei

Identifying machine learning techniques for classification of target advertising

Jin-A Choi, Kiho Lim
2020 ICT Express  
The paper also identifies an underexamined area, algorithm-based detection of click frauds, to illustrate how machine learning approaches can be integrated to preserve the viability of online advertising  ...  There have been numerous applications of artificial intelligence (AI) technologies to online advertising, especially to optimize the reach of target audiences.  ...  large data sets containing information regarding past impressions, clicks, searches, and purchases [20] .  ... 
doi:10.1016/j.icte.2020.04.012 fatcat:5qnbssw625chhfeeqkwzkgcjxm
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