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Modeling Dynamic Attributes for Next Basket Recommendation [article]

Yongjun Chen, Jia Li, Chenghao Liu, Chenxi Li, Markus Anderle, Julian McAuley, Caiming Xiong
2021 arXiv   pre-print
We argue that modeling such dynamic attributes can boost recommendation performance.  ...  Traditional approaches to next item and next basket recommendation typically extract users' interests based on their past interactions and associated static contextual information (e.g. a user id or item  ...  We evaluate AnDa on three real-world datasets and demonstrate the usefulness of modeling dynamic attributes for next basket recommendation.  ... 
arXiv:2109.11654v1 fatcat:cv4n3tyi2faj3iady5qbtxcugi

Next basket Recommendation Model Based on Attribute-aware Multi-level Attention

Tong Liu, Xianrui Yin, Weijian Ni
2020 IEEE Access  
CONCLUSION In this paper, a novel Next basket Recommendation Model Based on Attribute-aware Multi-level Attention was proposed.  ...  THE PROPOSED APPROACH In this paper, the Next basket Recommendation Model Based on Attribute-aware Multi-level Attention is proposed.  ... 
doi:10.1109/access.2020.3018030 fatcat:3dvf6aw2wfaahbbwqyqff62dae

A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation

Zhiqiang Pan, Wanyu Chen, Honghui Chen
2020 IEEE Access  
Different from general recommendation methods, sequential recommendation is proposed to model users' dynamic interest [16] [17] [18] . For example, Hidasi et al.  ...  FIGURE 6 : 6 For example, as for user B, the basket 0 and basket This work is licensed under a Creative Commons Attribution 4.0 License.  ...  His research interests include recommender system and information retrieval.  ... 
doi:10.1109/access.2020.3045092 fatcat:4yqyfbmwabhgtd6fjfah7zkobi

A Systematic Study on a Customer's Next-Items Recommendation Techniques

Qazi Mudassar Ilyas, Abid Mehmood, Ashfaq Ahmad, Muneer Ahmad
2022 Sustainability  
A customer's next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit.  ...  The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past.  ...  customs to recommend for the next basket [41, 42] .  ... 
doi:10.3390/su14127175 fatcat:fydrheys3zd4fesibokorcprqq

Pre-training of Context-aware Item Representation for Next Basket Recommendation [article]

Jingxuan Yang, Jun Xu, Jianzhuo Tong, Sheng Gao, Jun Guo, Jirong Wen
2019 arXiv   pre-print
Next basket recommendation, which aims to predict the next a few items that a user most probably purchases given his historical transactions, plays a vital role in market basket analysis.  ...  Inspired by the pre-trained representations of BERT in natural language processing, we propose to conduct context-aware item representation for next basket recommendation, called Item Encoder Representations  ...  Existing methods for next basket recommendation focus on modeling these two factors.  ... 
arXiv:1904.12604v1 fatcat:au5t5hyhnfae7nvcocndobtmge

Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks [article]

Elena Smirnova, Flavian Vasile
2017 arXiv   pre-print
Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation.  ...  modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context  ...  For example, if the user just added a product to basket, then he/she is very likely to buy it next.  ... 
arXiv:1706.07684v1 fatcat:owj72564ergn3emey27k2hnueq

Automated Market Basket Analysis System

Izang A., Okoro U., Olarewaju T., Fasanu T., Adeyinka A.
2018 International Journal of Computer Applications  
The Automated Market Basket Analysis System would improve on search methodologies that can also be of help in generating recommendations for consumers though the association rule mining algorithm embedded  ...  An automated MBA system was implemented through the use of the spiral model development model and a combination of HTML, PHP and MySQL as the programming environment to make this system web-based.  ...  Also, the system should be able to make recommendations the next time the customer comes online to make purchases.  ... 
doi:10.5120/ijca2018917043 fatcat:tdehbxbawrd7rgyveedtspu2oi

A Survey on Session-based Recommender Systems [article]

Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Defu Lian
2021 arXiv   pre-print
Different from other RSs such as content-based RSs and collaborative filtering-based RSs which usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences  ...  Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy.  ...  Yan Zhao for their constructive suggestions on this work. This work was supported by Australian Research Council Discovery Grants (DP180102378, DP190101079 and FT190100734).  ... 
arXiv:1902.04864v3 fatcat:oka5bvibzzbk5oreltrupehaey

Correlation-Sensitive Next-Basket Recommendation

Duc-Trong Le, Hady W. Lauw, Yuan Fang
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
Extensive experiments on three public real-life datasets showcase the effectiveness of our approach for the next-basket recommendation problem.  ...  Instead of recommending items independently for the next basket, we hypothesize that incorporating information on pairwise correlations among items would help to arrive at more coherent basket recommendations  ...  Yu et al., 2016] is a dynamic recurrent model, where a basket representation is aggregated by items' embedding via a pooling layer.  ... 
doi:10.24963/ijcai.2019/389 dblp:conf/ijcai/LeLF19 fatcat:pywr2sl4wnhw3gszn4fdx5hm3u

A Next Basket Recommendation Reality Check [article]

Ming Li, Sami Jullien, Mozhdeh Ariannezhad, Maarten de Rijke
2021 arXiv   pre-print
The goal of a next basket recommendation (NBR) system is to recommend items for the next basket for a user, based on the sequence of their prior baskets.  ...  We provide a novel angle on the evaluation of next basket recommendation methods, centered on the distinction between repetition and exploration: the next basket is typically composed of previously consumed  ...  Therefore, models designed for item-based recommendation are not fit for basket-based recommendation, and dedicated NBR methods have been proposed [6, 14] .  ... 
arXiv:2109.14233v1 fatcat:viadfgzvije4nh7k7he7svakeq

Classification of the User's Intent Detection in Ecommerce systems – Survey and Recommendations

Marek Koniew, Institute of Informatics, Silesian University of Technology, Gliwice, Poland
2020 International Journal of Information Engineering and Electronic Business  
We find that various aspects of customer intent detection can be tackled by leveraging tremendous recent recommendation systems' progress.  ...  In this work, we review existing works from different domains that can be re-used for customer intent detection in the e-commerce.  ...  To address that, we propose the following four clear classes of intent detection: Next-Item Recommendation, Market Basket Analysis, Propensity Modelling, and Customer Lifetime Value.  ... 
doi:10.5815/ijieeb.2020.06.01 fatcat:zcooazhm2jcqfmmegq7syyj7oq

Following Good Examples - Health Goal-Oriented Food Recommendation based on Behavior Data

Yabo Ling, Jian-Yun Nie, Daiva Nielsen, Bärbel Knäuper, Nathan Yang, Laurette Dubé
2022 Proceedings of the ACM Web Conference 2022  
By combining such a goal-oriented recommendation model with a general model, the recommendations can be smoothly tuned toward the goal without disruptive food changes.  ...  Typical recommender systems try to mimic the past behaviors of users to make future recommendations. For example, in food recommendations, they tend to recommend the foods the user prefers.  ...  Many models are proposed based on RNNs in Next-basket recommendation, such as sessionbased GRU with ranking loss (GRU4Rec) [36] , Dynamic REcurrent bAsket Model (DREAM) [49] and Attribute-aware Neural  ... 
doi:10.1145/3485447.3514193 fatcat:uhxoaspcvvd5xhctvstnpfmr4q

M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation

Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning
2022 IEEE Transactions on Knowledge and Data Engineering  
In this paper, we develop a novel mixed model with preferences, popularities and transitions (M 2 ) for the next-basket recommendation.  ...  Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole.  ...  This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see  ... 
doi:10.1109/tkde.2022.3142773 fatcat:arsu5rqhjze3ndhwzd4et3trz4

Identifying Substitute and Complementary Products for Assortment Optimization with Cleora Embeddings [article]

Sergiy Tkachuk, Anna Wróblewska, Jacek Dąbrowski, Szymon Łukasik
2022 arXiv   pre-print
., substitutes and complements is essential for users' recommendations at the vendor's site and for the vendor - to perform efficient assortment optimization.  ...  The paper introduces a novel method for finding products' substitutes and complements based on the graph embedding Cleora algorithm.  ...  the European Regional Development Fund under the Regional Operational Program of the Małopolska Region for 2014-2020.  ... 
arXiv:2208.06262v1 fatcat:ze23o3hec5f43fwkovpib5rrje

A personalized recommender system based on web usage mining and decision tree induction

Yoon Ho Cho, Jae Kyeong Kim, Soung Hie Kim
2002 Expert systems with applications  
For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies. q  ...  would lead to poor recommendations.  ...  The next recommendation phases continue to be performed only for customers in the model candidate set who have 1 as the value of y. Example 5.  ... 
doi:10.1016/s0957-4174(02)00052-0 fatcat:ojlshwwqdjarxmwho4g3vvii2u
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