Multi-modal Preference Modeling for Product Search

Guo, Yangyang; Cheng, Zhiyong; Nie, Liqiang; Xu, Xin-Shun; Kankanhalli, Mohan

doi:10.1145/3240508.3240541

Cited by 61 publications

(44 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meng et al [32] incorporated other users' emotions towards a review to calculate the importance of this review in the training of matrix factorization model. Some methods concatenate all the reviews belonging to a user (or item) as a user (or item) document, and then employ deep learning methods to learn the continuous vector representation for the user (or item) [4,19,48,51]. For example, Transnets [4] and DeepCoNN [51] process the user and item documents with convolutional neural network to generate the vector representation for users and items.…”

Section: Review-level Methodsmentioning

confidence: 99%

Attentive Aspect Modeling for Review-Aware Recommendation

Guan

Cheng

et al. 2019

ACM Trans. Inf. Syst.

Self Cite

100

View full text Add to dashboard Cite

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.

show abstract

Section: Review-level Methodsmentioning

confidence: 99%

Attentive Aspect Modeling for Review-Aware Recommendation

Guan

Cheng

et al. 2019

ACM Trans. Inf. Syst.

Self Cite

100

View full text Add to dashboard Cite

show abstract

“…Among all the methods, SCEM and SCRM3 perform better than all the other baselines without using short-term context, including their corresponding retrieval baseline, Table 1: Performance of our short-term context embedding model (SCEM) and baselines when re-ranking from the 2nd page. The number is the relative improvement of each method compared with the production model (PROD) 4 . ' − ' indicates significant worse than SCEM in paired student t-test with p ≤ 0.001.…”

Section: Methodsmentioning

confidence: 99%

A Study of Context Dependencies in Multi-page Product Search

Teo

Dattatreya

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

In product search, users tend to browse results on multiple search result pages (SERPs) (e.g., for queries on clothing and shoes) before deciding which item to purchase. Users' clicks can be considered as implicit feedback which indicates their preferences and used to re-rank subsequent SERPs. Relevance feedback (RF) techniques are usually involved to deal with such scenarios. However, these methods are designed for document retrieval, where relevance is the most important criterion. In contrast, product search engines need to retrieve items that are not only relevant but also satisfactory in terms of customers' preferences. Personalization based on users' purchase history has been shown to be effective in product search [1]. However, this method captures users' long-term interest, which do not always align with their short-term interest, and does not benefit customers with little or no purchase history. In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search. We also propose an end-to-end context-aware embedding model which can capture both types of context. Our experimental results show that short-term context leads to much better performance compared with long-term and no context. Moreover, our proposed model is more effective than state-of-art word-based RF models.

show abstract

“…Sparsity problem is one of the major problems encountered by recommendation systems, and the data sparsity has a great impact on the quality of the recommendation [1]. Several attempts have been made to mitigate the negative effects of sparse data [2,15,[24][25][26]. Pazzani et al [24] alleviated the problem of sparse user interaction by introducing additional information of users.…”

Section: Sparsity In Recommendation Systemmentioning

confidence: 99%

Personalized Item Recommendation for Second-hand Trading Platform

Gan

Wei

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

Self Cite

View full text Add to dashboard Cite

With rising awareness of environment protection and recycling, secondhand trading platforms have attracted increasing attention in recent years. The interaction data on secondhand trading platforms, consisting of sufficient interactions per user but rare interactions per item, is different from what they are on traditional platforms. Therefore, building successful recommendation systems in the secondhand trading platforms requires balancing modeling items' and users' preference, and mitigating the adverse effects of the sparsity, which makes recommendation especially challenging. Accordingly, we proposed a method to simultaneously learn representations of items and users from coarse-grained and fine-grained features, and a multi-task learning strategy is designed to address the issue of data sparsity. Experiments conducted on a real-world secondhand trading platform dataset demonstrate the effectiveness of our proposed model.

show abstract

Multi-modal Preference Modeling for Product Search

Cited by 61 publications

References 32 publications

Attentive Aspect Modeling for Review-Aware Recommendation

Attentive Aspect Modeling for Review-Aware Recommendation

A Study of Context Dependencies in Multi-page Product Search

Personalized Item Recommendation for Second-hand Trading Platform

Contact Info

Product

Resources

About