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

Basket-Sensitive Item Recommendation (BSIR) is a challenging task that aims to recommend an item to add to the current basket given a user’s historical behaviors. The recommended item is supposed to be relevant to the items in current basket. Previous works mainly produce a recommendation...

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Autores principales:	Zhiqiang Pan, Wanyu Chen, Honghui Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Basket-sensitive item recommendation sequential recommendation recurrent neural networks attention mechanism Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/1867ccc312da489bbd645f3a8c2aa8dd
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id	oai:doaj.org-article:1867ccc312da489bbd645f3a8c2aa8dd
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spelling	oai:doaj.org-article:1867ccc312da489bbd645f3a8c2aa8dd2021-11-26T00:00:14ZA Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation2169-353610.1109/ACCESS.2020.3045092https://doaj.org/article/1867ccc312da489bbd645f3a8c2aa8dd2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9294065/https://doaj.org/toc/2169-3536Basket-Sensitive Item Recommendation (BSIR) is a challenging task that aims to recommend an item to add to the current basket given a user’s historical behaviors. The recommended item is supposed to be relevant to the items in current basket. Previous works mainly produce a recommendation based on user’s current basket, ignoring the inherent preference released by user’s long-term behaviors and failing to accurately distinguish the item importance in the basket for detecting user intent. To tackle the above issues, we propose a hybrid model, i.e., Hybrid-Preference Neural Model (HPNM), where a user’s inherent preference is recognized by modeling the historical sequential baskets and the recent preference is identified by focusing on the current basket. In detail, we apply an attention mechanism for distinguishing the importance of items in a basket to generate an accurate basket representation. GRU is utilized for modeling the basket-level sequential information to obtain user’s long-term preference and the representation of the current session is regarded as user’s short-term preference. We evaluate the performance of our proposals against the state-of-the-art baselines in the field of BSIR on two public datasets, i.e., TaFeng and Foursquare. The experimental results show that HPNM can achieve obvious improvements against the baselines in terms of HLU and Recall. In addition, we find HPNM with an attention mechanism can lead to a larger improvement against the baseline for item recommendation in terms of HLU and Recall on testing baskets with relatively fewer items.Zhiqiang PanWanyu ChenHonghui ChenIEEEarticleBasket-sensitive item recommendationsequential recommendationrecurrent neural networksattention mechanismElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 226131-226141 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Basket-sensitive item recommendation sequential recommendation recurrent neural networks attention mechanism Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Basket-sensitive item recommendation sequential recommendation recurrent neural networks attention mechanism Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zhiqiang Pan Wanyu Chen Honghui Chen A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation
description	Basket-Sensitive Item Recommendation (BSIR) is a challenging task that aims to recommend an item to add to the current basket given a user’s historical behaviors. The recommended item is supposed to be relevant to the items in current basket. Previous works mainly produce a recommendation based on user’s current basket, ignoring the inherent preference released by user’s long-term behaviors and failing to accurately distinguish the item importance in the basket for detecting user intent. To tackle the above issues, we propose a hybrid model, i.e., Hybrid-Preference Neural Model (HPNM), where a user’s inherent preference is recognized by modeling the historical sequential baskets and the recent preference is identified by focusing on the current basket. In detail, we apply an attention mechanism for distinguishing the importance of items in a basket to generate an accurate basket representation. GRU is utilized for modeling the basket-level sequential information to obtain user’s long-term preference and the representation of the current session is regarded as user’s short-term preference. We evaluate the performance of our proposals against the state-of-the-art baselines in the field of BSIR on two public datasets, i.e., TaFeng and Foursquare. The experimental results show that HPNM can achieve obvious improvements against the baselines in terms of HLU and Recall. In addition, we find HPNM with an attention mechanism can lead to a larger improvement against the baseline for item recommendation in terms of HLU and Recall on testing baskets with relatively fewer items.
format	article
author	Zhiqiang Pan Wanyu Chen Honghui Chen
author_facet	Zhiqiang Pan Wanyu Chen Honghui Chen
author_sort	Zhiqiang Pan
title	A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation
title_short	A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation
title_full	A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation
title_fullStr	A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation
title_full_unstemmed	A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation
title_sort	hybrid-preference neural model for basket-sensitive item recommendation
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/1867ccc312da489bbd645f3a8c2aa8dd
work_keys_str_mv	AT zhiqiangpan ahybridpreferenceneuralmodelforbasketsensitiveitemrecommendation AT wanyuchen ahybridpreferenceneuralmodelforbasketsensitiveitemrecommendation AT honghuichen ahybridpreferenceneuralmodelforbasketsensitiveitemrecommendation AT zhiqiangpan hybridpreferenceneuralmodelforbasketsensitiveitemrecommendation AT wanyuchen hybridpreferenceneuralmodelforbasketsensitiveitemrecommendation AT honghuichen hybridpreferenceneuralmodelforbasketsensitiveitemrecommendation
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A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation

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