Next Basket Recommendation Model Based on Attribute-Aware Multi-Level Attention

Liu, Tong; Yin, Xianrui; Ni, Weijian

doi:10.1109/access.2020.3018030

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…CF technique is an essential component of most NBR systems, in which data from the user's preferences (i.e., customers' ratings) is joined with that of other users to predict what additional items the user might like. The general concept of this technique is based on observation of the preferences of other users that are similar to the historical preferences of the target user or finding items that are similar to the items the user liked earlier [15]. Later on, deep recommender systems that extract deep features in a supervised or unsupervised manner have been presented by researchers in order to capture more reasonable similarities and implicit relationships between items [16], [17].…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Approach for Rational Next-Basket Recommendation

Fouad¹,

Hussein

Rady

et al. 2022

IEEE Access

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E-commerce is one of the most valuable and popular application scenarios of recommendation technology. In this context, making rational decisions is required due to the necessity of objectivity and decision rationale tracking. However, a significant number of irrational recommendations may be generated as a result of using a narrow range of interestingness criteria, considering inconsistent user preferences, overlooking the temporal weights of items, and using outlier choices in making a prediction. In this paper, we propose an efficient approach called MONBR (Multi-Objective Next-Basket Recommendation) to improve the quality of recommendations. An approach promotes rational recommendations by measuring the temporal importance of items to the user using several interestingness criteria, namely utility, popularity, stability, frequency, occupancy, and novelty. Moreover, combining the multi-criteria weights of items obtained from the MONBR approach with the item-based collaborative filtering technique results in more accurate and reasonable recommendations. Experiment results on real-world datasets demonstrate that the proposed approach improves the quality of recommendations. The average quality of recommendations derived from the proposed approach is up to 60.95% more accurate than state-of-the-art algorithms with a recommended basket size ranging from 10 to 100. Furthermore, the results highlight the importance of adapting objectivity and rationality in the recommendation process to meet the needs of as many users as possible. The average user-level performance is up to 10.21% higher on dense datasets.

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Section: Related Workmentioning

confidence: 99%

“…Nonetheless, users often need more rational results in ecommerce systems and are not satisfied with the similarity concept between items or users [2]. Moreover, most CF based algorithms ignore the sequential characteristics of historical transactions and cannot display users' short-term preferences [15].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Approach for Rational Next-Basket Recommendation

Fouad¹,

Hussein

Rady

et al. 2022

IEEE Access

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show abstract

“…An adaptive attention mechanism has been combined with an RNN. Similarly, Liu et al [105] focus on the attributes of items within a basket and exploit their relationships within a single basket and the past baskets of the user to improve the recommendations. Dau and Salim [106] have utilized a topic model to extract the domain-specific aspect of the product and associated sentiment lexicons fed into an LSTM encoder via an interactive neural attention mechanism.…”

Section: Hybrid Network (Attention+)mentioning

confidence: 99%

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

et al. 2022

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A customer’s next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit. The recommendations made by these systems support businesses by increasing their revenue and providing a more personalized shopping experience to customers. The main objective of this paper is to provide a systematic literature review of the domain to analyze the recent techniques and assist future research. The paper examined 90 selected studies to answer the research questions concerning the key aspects of NIRSs. To this end, the main contribution of the paper is that it provides detailed insight into the use of conventional and deep learning techniques, the popular datasets, and specialized metrics for developing and evaluating these systems. The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past. However, more recent works have mainly focused on deep learning techniques due to their enhanced ability to learn sequential and temporal information. Some of the challenges in developing NIRSs that need further investigation are related to cold start, data sparsity, and cross-domain recommendations.

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“…The collaborative filtering (CF) technique is a key component of most recommendation systems, in which data from the user's preferences is joined with that of other users to predict what additional items the user may want [1]. This technique is based on observing other users' preferences that are similar to the target user's historical preferences or finding items that are considered similar to the items the user liked previously [2]. CF-based systems suggest items to the user depending on the ratings of those other users.…”

Section: Introductionmentioning

confidence: 99%

“…This technique is unstable when there is not enough information about the user or the item to recommend, as it suffers from data sparsity and cold start issues [3]. Moreover, most CF-based algorithms ignore the sequential characteristics of historical transactions and are unable to display users' short-term preferences [2]. These issues eventually lead to a reduction in the quality or effectiveness of the outcomes.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Recommender System Combining Collaborative Filtering with Utility Mining

fouad¹,

Hussein

Rady

et al. 2022

IJICIS

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Based on a variety of information sources, recommender systems can identify specific items for various user interests. Techniques for recommender systems are classified into two types: personalized and non-personalized. Personalized algorithms are based on individual user preferences or collaborative filtering data; as the system learns more about the user, the recommendations will become more satisfying. They do, however, suffer from data sparsity and cold start issues. On the other hand, non-personalized algorithms make recommendations based on the importance of the items in the database; they are very useful when the system has no information about a specific user. Their accuracy, however, is limited by the issue of personalization. In most cases, one of the recommendation categories can be used to make recommendations. Yet, it is a challenge to evaluate the importance of items to the user while simultaneously using personalized and non-personalized preferences functions and ranking a set of candidate items based on these functions. This paper addresses this issue and improves recommendation quality by introducing a new hybrid recommendation technique. The proposed hybrid recommendation technique combines the importance of items to the user obtained by the utility mining method with the similarity weights of items produced by the collaborative filtering technique to make the recommendation process more reasonable and accurate. This technique can provide appropriate recommendations whether or not users have previous purchasing histories. Finally, experimental results show that the proposed hybrid recommendation technique outperforms both implemented collaborative filtering and utility-based recommendation techniques.

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Next Basket Recommendation Model Based on Attribute-Aware Multi-Level Attention

Cited by 10 publications

References 25 publications

An Efficient Approach for Rational Next-Basket Recommendation

An Efficient Approach for Rational Next-Basket Recommendation

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

A Hybrid Recommender System Combining Collaborative Filtering with Utility Mining

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